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Assured cloud computing / / edited by Roy H. Campbell, Charles A. Kamhoua, Kevin A. Kwiat
Assured cloud computing / / edited by Roy H. Campbell, Charles A. Kamhoua, Kevin A. Kwiat
Autore Campbell Roy
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : IEEE Computer Society, Inc./Wiley, , 2018
Descrizione fisica 1 online resource (363 pages)
Disciplina 004.67/82
Soggetto topico Cloud computing
ISBN 1-119-42850-5
1-119-42848-3
1-119-42849-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface xiii -- Editors’ Biographies xvii -- List of Contributors xix -- 1 Introduction 1 /Roy H. Campbell -- 1.1 Introduction 1 -- 1.1.1 Mission-Critical Cloud Solutions for the Military 2 -- 1.2 Overview of the Book 3 -- 2 Survivability: Design, Formal Modeling, and Validation of Cloud Storage Systems Using Maude 10 /Rakesh Bobba, Jon Grov, Indranil Gupta, Si Liu, José Meseguer,Peter Csaba Ölveczky, and Stephen Skeirik -- 2.1 Introduction 10 -- 2.1.1 State of the Art 11 -- 2.1.2 Vision: Formal Methods for Cloud Storage Systems 12 -- 2.1.3 The Rewriting Logic Framework 13 -- 2.1.4 Summary: Using Formal Methods on Cloud Storage Systems 15 -- 2.2 Apache Cassandra 17 -- 2.3 Formalizing, Analyzing, and Extending Google’s Megastore 23 -- 2.3.1 Specifying Megastore 23 -- 2.3.2 Analyzing Megastore 25 -- 2.3.2.1 Megastore-CGC 29 -- 2.4 RAMP Transaction Systems 30 -- 2.5 Group Key Management via ZooKeeper 31 -- 2.5.1 ZooKeeper Background 32 -- 2.5.2 System Design 33 -- 2.5.3 Maude Model 34 -- 2.5.4 Analysis and Discussion 35 -- 2.6 How Amazon Web Services Uses Formal Methods 37 -- 2.6.1 Use of Formal Methods 37 -- 2.6.2 Outcomes and Experiences 38 -- 2.6.3 Limitations 39 -- 2.7 Related Work 40 -- 2.8 Concluding Remarks 42 -- 2.8.1 The Future 43 -- 3 Risks and Benefits: Game-Theoretical Analysis and Algorithm for Virtual Machine Security Management in the Cloud 49 /Luke Kwiat, Charles A. Kamhoua, Kevin A. Kwiat, and Jian Tang -- 3.1 Introduction 49 -- 3.2 Vision: Using Cloud Technology in Missions 51 -- 3.3 State of the Art 54 -- 3.4 System Model 57 -- 3.5 Game Model 59 -- 3.6 Game Analysis 61 -- 3.7 Model Extension and Discussion 67 -- 3.8 Numerical Results and Analysis 71 -- 3.8.1 Changes in User 2’s Payoff with Respect to L2 71 -- 3.8.2 Changes in User 2’s Payoff with Respect to e 72 -- 3.8.3 Changes in User 2’s Payoff with Respect to π 73 -- 3.8.4 Changes in User 2’s Payoff with Respect to qI 74 -- 3.8.5 Model Extension to n = 10 Users 75.
3.9 The Future 78 -- 4 Detection and Security: Achieving Resiliency by Dynamic and Passive System Monitoring and Smart Access Control 81 /Zbigniew Kalbarczyk -- 4.1 Introduction 82 -- 4.2 Vision: Using Cloud Technology in Missions 83 -- 4.3 State of the Art 84 -- 4.4 Dynamic VM Monitoring Using Hypervisor Probes 85 -- 4.4.1 Design 86 -- 4.4.2 Prototype Implementation 88 -- 4.4.3 Example Detectors 90 -- 4.4.3.1 Emergency Exploit Detector 90 -- 4.4.3.2 Application Heartbeat Detector 91 -- 4.4.4 Performance 93 -- 4.4.4.1 Microbenchmarks 93 -- 4.4.4.2 Detector Performance 94 -- 4.4.5 Summary 95 -- 4.5 Hypervisor Introspection: A Technique for Evading Passive Virtual Machine Monitoring 96 -- 4.5.1 Hypervisor Introspection 97 -- 4.5.1.1 VMI Monitor 97 -- 4.5.1.2 VM Suspend Side-Channel 97 -- 4.5.1.3 Limitations of Hypervisor Introspection 98 -- 4.5.2 Evading VMI with Hypervisor Introspection 98 -- 4.5.2.1 Insider Attack Model and Assumptions 98 -- 4.5.2.2 Large File Transfer 99 -- 4.5.3 Defenses against Hypervisor Introspection 101 -- 4.5.3.1 Introducing Noise to VM Clocks 101 -- 4.5.3.2 Scheduler-Based Defenses 101 -- 4.5.3.3 Randomized Monitoring Interval 102 -- 4.5.4 Summary 103 -- 4.6 Identifying Compromised Users in Shared Computing Infrastructures 103 -- 4.6.1 Target System and Security Data 104 -- 4.6.1.1 Data and Alerts 105 -- 4.6.1.2 Automating the Analysis of Alerts 106 -- 4.6.2 Overview of the Data 107 -- 4.6.3 Approach 109 -- 4.6.3.1 The Model: Bayesian Network 109 -- 4.6.3.2 Training of the Bayesian Network 110 -- 4.6.4 Analysis of the Incidents 112 -- 4.6.4.1 Sample Incident 112 -- 4.6.4.2 Discussion 113 -- 4.6.5 Supporting Decisions with the Bayesian Network Approach 114 -- 4.6.5.1 Analysis of the Incidents 114 -- 4.6.5.2 Analysis of the Borderline Cases 116 -- 4.6.6 Conclusion 118 -- 4.7 Integrating Attribute-Based Policies into Role-Based Access Control 118 -- 4.7.1 Framework Description 119 -- 4.7.2 Aboveground Level: Tables 119 -- 4.7.2.1 Environment 120.
4.7.2.2 User-Role Assignments 120 -- 4.7.2.3 Role-Permission Assignments 121 -- 4.7.3 Underground Level: Policies 121 -- 4.7.3.1 Role-Permission Assignment Policy 122 -- 4.7.3.2 User-Role Assignment Policy 123 -- 4.7.4 Case Study: Large-Scale ICS 123 -- 4.7.4.1 RBAC Model-Building Process 124 -- 4.7.4.2 Discussion of Case Study 127 -- 4.7.5 Concluding Remarks 128 -- 4.8 The Future 128 -- 5 Scalability, Workloads, and Performance: Replication, Popularity, Modeling, and Geo-Distributed File Stores 133 /Roy H. Campbell, Shadi A. Noghabi, and Cristina L. Abad -- 5.1 Introduction 133 -- 5.2 Vision: Using Cloud Technology in Missions 134 -- 5.3 State of the Art 136 -- 5.4 Data Replication in a Cloud File System 137 -- 5.4.1 MapReduce Clusters 138 -- 5.4.1.1 File Popularity, Temporal Locality, and Arrival Patterns 142 -- 5.4.1.2 Synthetic Workloads for Big Data 144 -- 5.4.2 Related Work 147 -- 5.4.3 Contribution from Our Approach to Generating Big Data Request Streams Using Clustered Renewal Processes 149 -- 5.4.3.1 Scalable Geo-Distributed Storage 149 -- 5.4.4 Related Work 151 -- 5.4.5 Summary of Ambry 152 -- 5.5 Summary 153 -- 5.6 The Future 153 -- 6 Resource Management: Performance Assuredness in Distributed Cloud Computing via Online Reconfigurations 160 /Mainak Ghosh, Le Xu, and Indranil Gupta -- 6.1 Introduction 161 -- 6.2 Vision: Using Cloud Technology in Missions 163 -- 6.3 State of the Art 164 -- 6.3.1 State of the Art: Reconfigurations in Sharded Databases/Storage 164 -- 6.3.1.1 Database Reconfigurations 164 -- 6.3.1.2 Live Migration 164 -- 6.3.1.3 Network Flow Scheduling 164 -- 6.3.2 State of the Art: Scale-Out/Scale-In in Distributed Stream Processing Systems 165 -- 6.3.2.1 Real-Time Reconfigurations 165 -- 6.3.2.2 Live Migration 165 -- 6.3.2.3 Real-Time Elasticity 165 -- 6.3.3 State of the Art: Scale-Out/Scale-In in Distributed Graph Processing Systems 166 -- 6.3.3.1 Data Centers 166 -- 6.3.3.2 Cloud and Storage Systems 166 -- 6.3.3.3 Data Processing Frameworks 166.
6.3.3.4 Partitioning in Graph Processing 166 -- 6.3.3.5 Dynamic Repartitioning in Graph Processing 167 -- 6.3.4 State of the Art: Priorities and Deadlines in Batch Processing Systems 167 -- 6.3.4.1 OS Mechanisms 167 -- 6.3.4.2 Preemption 167 -- 6.3.4.3 Real-Time Scheduling 168 -- 6.3.4.4 Fairness 168 -- 6.3.4.5 Cluster Management with SLOs 168 -- 6.4 Reconfigurations in NoSQL and Key-Value Storage/Databases 169 -- 6.4.1 Motivation 169 -- 6.4.2 Morphus: Reconfigurations in Sharded Databases/Storage 170 -- 6.4.2.1 Assumptions 170 -- 6.4.2.2 MongoDB System Model 170 -- 6.4.2.3 Reconfiguration Phases in Morphus 171 -- 6.4.2.4 Algorithms for Efficient Shard Key Reconfigurations 172 -- 6.4.2.5 Network Awareness 175 -- 6.4.2.6 Evaluation 175 -- 6.4.3 Parqua: Reconfigurations in Distributed Key-Value Stores 179 -- 6.4.3.1 System Model 180 -- 6.4.3.2 System Design and Implementation 181 -- 6.4.3.3 Experimental Evaluation 183 -- 6.5 Scale-Out and Scale-In Operations 185 -- 6.5.1 Stela: Scale-Out/Scale-In in Distributed Stream Processing Systems 186 -- 6.5.1.1 Motivation 186 -- 6.5.1.2 Data Stream Processing Model and Assumptions 187 -- 6.5.1.3 Stela: Scale-Out Overview 187 -- 6.5.1.4 Effective Throughput Percentage (ETP) 188 -- 6.5.1.5 Iterative Assignment and Intuition 190 -- 6.5.1.6 Stela: Scale-In 191 -- 6.5.1.7 Core Architecture 191 -- 6.5.1.8 Evaluation 193 -- 6.5.1.9 Experimental Setup 193 -- 6.5.1.10 Yahoo! Storm Topologies and Network Monitoring Topology 193 -- 6.5.1.11 Convergence Time 195 -- 6.5.1.12 Scale-In Experiments 196 -- 6.5.2 Scale-Out/Scale-In in Distributed Graph Processing Systems 197 -- 6.5.2.1 Motivation 197 -- 6.5.2.2 What to Migrate, and How? 199 -- 6.5.2.3 When to Migrate? 201 -- 6.5.2.4 Evaluation 203 -- 6.6 Priorities and Deadlines in Batch Processing Systems 204 -- 6.6.1 Natjam: Supporting Priorities and Deadlines in Hadoop 204 -- 6.6.1.1 Motivation 204 -- 6.6.1.2 Eviction Policies for a Dual-Priority Setting 206 -- 6.6.1.3 Natjam Architecture 209.
6.6.1.4 Natjam-R: Deadline-Based Eviction 215 -- 6.6.1.5 Microbenchmarks 216 -- 6.6.1.6 Natjam-R Evaluation 221 -- 6.7 Summary 223 -- 6.8 The Future 224 -- 7 Theoretical Considerations: Inferring and Enforcing Use Patterns for Mobile Cloud Assurance 237 /Gul Agha, Minas Charalambides, Kirill Mechitov, Karl Palmskog,Atul Sandur, and Reza Shiftehfar -- 7.1 Introduction 237 -- 7.2 Vision 239 -- 7.3 State of the Art 240 -- 7.3.1 Code Offloading 241 -- 7.3.2 Coordination Constraints 241 -- 7.3.3 Session Types 242 -- 7.4 Code Offloading and the IMCM Framework 243 -- 7.4.1 IMCM Framework: Overview 244 -- 7.4.2 Cloud Application and Infrastructure Models 244 -- 7.4.3 Cloud Application Model 245 -- 7.4.4 Defining Privacy for Mobile Hybrid Cloud Applications 247 -- 7.4.5 A Face Recognition Application 247 -- 7.4.6 The Design of an Authorization System 249 -- 7.4.7 Mobile Hybrid Cloud Authorization Language 250 -- 7.4.7.1 Grouping, Selection, and Binding 252 -- 7.4.7.2 Policy Description 252 -- 7.4.7.3 Policy Evaluation 253 -- 7.4.8 Performance- and Energy-Usage-Based Code Offloading 254 -- 7.4.8.1 Offloading for Sequential Execution on a Single Server 254 -- 7.4.8.2 Offloading for Parallel Execution on Hybrid Clouds 255 -- 7.4.8.3 Maximizing Performance 255 -- 7.4.8.4 Minimizing Energy Consumption 256 -- 7.4.8.5 Energy Monitoring 257 -- 7.4.8.6 Security Policies and Energy Monitoring 258 -- 7.5 Coordinating Actors 259 -- 7.5.1 Expressing Coordination 259 -- 7.5.1.1 Synchronizers 260 -- 7.5.1.2 Security Issues in Synchronizers 260 -- 7.6 Session Types 264 -- 7.6.1 Session Types for Actors 265 -- 7.6.1.1 Example: Sliding Window Protocol 265 -- 7.6.2 Global Types 266 -- 7.6.3 Programming Language 268 -- 7.6.4 Local Types and Type Checking 269 -- 7.6.5 Realization of Global Types 270 -- 7.7 The Future 271 -- Acknowledgments 272 -- 8 Certifications Past and Future: A Future Model for Assigning Certifications that Incorporate Lessons Learned from Past Practices 277 /Masooda Bashir, Carlo Di Giulio, and Charles A. Kamhoua.
8.1 Introduction 277 -- 8.1.1 What Is a Standard? 279 -- 8.1.2 Standards and Cloud Computing 281 -- 8.2 Vision: Using Cloud Technology in Missions 283 -- 8.3 State of the Art 284 -- 8.3.1 The Federal Risk Authorization Management Program 286 -- 8.3.2 SOC Reports and TSPC 288 -- 8.3.3 ISO/IEC 27001 291 -- 8.3.4 Main Differences among the Standards 292 -- 8.3.5 Other Existing Frameworks 293 -- 8.3.5.1 PCI-DSS 293 -- 8.3.5.2 C5 294 -- 8.3.5.3 STAR 294 -- 8.3.6 What Protections Do Standards Offer against Vulnerabilities in the Cloud? 294 -- 8.4 Comparison among Standards 296 -- 8.4.1 Strategy for Comparing Standards 298 -- 8.4.2 Patterns, Anomalies, and Discoveries 299 -- 8.5 The Future 302 -- 8.5.1 Current Challenges 304 -- 8.5.2 Opportunities 305 -- 9 Summary and Future Work 312 /Roy H. Campbell -- 9.1 Survivability 312 -- 9.2 Risks and Benefits 313 -- 9.3 Detection and Security 314 -- 9.4 Scalability, Workloads, and Performance 316 -- 9.5 Resource Management 319 -- 9.6 Theoretical Considerations: Inferring and Enforcing Use Patterns for Mobile Cloud Assurance 321 -- 9.7 Certifications 322 -- Index 327.
Record Nr. UNINA-9910555182003321
Campbell Roy  
Hoboken, New Jersey : , : IEEE Computer Society, Inc./Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Assured cloud computing / / edited by Roy H. Campbell, Charles A. Kamhoua, Kevin A. Kwiat
Assured cloud computing / / edited by Roy H. Campbell, Charles A. Kamhoua, Kevin A. Kwiat
Autore Campbell Roy
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : IEEE Computer Society, Inc./Wiley, , 2018
Descrizione fisica 1 online resource (363 pages)
Disciplina 004.67/82
Soggetto topico Cloud computing
ISBN 1-119-42850-5
1-119-42848-3
1-119-42849-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface xiii -- Editors’ Biographies xvii -- List of Contributors xix -- 1 Introduction 1 /Roy H. Campbell -- 1.1 Introduction 1 -- 1.1.1 Mission-Critical Cloud Solutions for the Military 2 -- 1.2 Overview of the Book 3 -- 2 Survivability: Design, Formal Modeling, and Validation of Cloud Storage Systems Using Maude 10 /Rakesh Bobba, Jon Grov, Indranil Gupta, Si Liu, José Meseguer,Peter Csaba Ölveczky, and Stephen Skeirik -- 2.1 Introduction 10 -- 2.1.1 State of the Art 11 -- 2.1.2 Vision: Formal Methods for Cloud Storage Systems 12 -- 2.1.3 The Rewriting Logic Framework 13 -- 2.1.4 Summary: Using Formal Methods on Cloud Storage Systems 15 -- 2.2 Apache Cassandra 17 -- 2.3 Formalizing, Analyzing, and Extending Google’s Megastore 23 -- 2.3.1 Specifying Megastore 23 -- 2.3.2 Analyzing Megastore 25 -- 2.3.2.1 Megastore-CGC 29 -- 2.4 RAMP Transaction Systems 30 -- 2.5 Group Key Management via ZooKeeper 31 -- 2.5.1 ZooKeeper Background 32 -- 2.5.2 System Design 33 -- 2.5.3 Maude Model 34 -- 2.5.4 Analysis and Discussion 35 -- 2.6 How Amazon Web Services Uses Formal Methods 37 -- 2.6.1 Use of Formal Methods 37 -- 2.6.2 Outcomes and Experiences 38 -- 2.6.3 Limitations 39 -- 2.7 Related Work 40 -- 2.8 Concluding Remarks 42 -- 2.8.1 The Future 43 -- 3 Risks and Benefits: Game-Theoretical Analysis and Algorithm for Virtual Machine Security Management in the Cloud 49 /Luke Kwiat, Charles A. Kamhoua, Kevin A. Kwiat, and Jian Tang -- 3.1 Introduction 49 -- 3.2 Vision: Using Cloud Technology in Missions 51 -- 3.3 State of the Art 54 -- 3.4 System Model 57 -- 3.5 Game Model 59 -- 3.6 Game Analysis 61 -- 3.7 Model Extension and Discussion 67 -- 3.8 Numerical Results and Analysis 71 -- 3.8.1 Changes in User 2’s Payoff with Respect to L2 71 -- 3.8.2 Changes in User 2’s Payoff with Respect to e 72 -- 3.8.3 Changes in User 2’s Payoff with Respect to π 73 -- 3.8.4 Changes in User 2’s Payoff with Respect to qI 74 -- 3.8.5 Model Extension to n = 10 Users 75.
3.9 The Future 78 -- 4 Detection and Security: Achieving Resiliency by Dynamic and Passive System Monitoring and Smart Access Control 81 /Zbigniew Kalbarczyk -- 4.1 Introduction 82 -- 4.2 Vision: Using Cloud Technology in Missions 83 -- 4.3 State of the Art 84 -- 4.4 Dynamic VM Monitoring Using Hypervisor Probes 85 -- 4.4.1 Design 86 -- 4.4.2 Prototype Implementation 88 -- 4.4.3 Example Detectors 90 -- 4.4.3.1 Emergency Exploit Detector 90 -- 4.4.3.2 Application Heartbeat Detector 91 -- 4.4.4 Performance 93 -- 4.4.4.1 Microbenchmarks 93 -- 4.4.4.2 Detector Performance 94 -- 4.4.5 Summary 95 -- 4.5 Hypervisor Introspection: A Technique for Evading Passive Virtual Machine Monitoring 96 -- 4.5.1 Hypervisor Introspection 97 -- 4.5.1.1 VMI Monitor 97 -- 4.5.1.2 VM Suspend Side-Channel 97 -- 4.5.1.3 Limitations of Hypervisor Introspection 98 -- 4.5.2 Evading VMI with Hypervisor Introspection 98 -- 4.5.2.1 Insider Attack Model and Assumptions 98 -- 4.5.2.2 Large File Transfer 99 -- 4.5.3 Defenses against Hypervisor Introspection 101 -- 4.5.3.1 Introducing Noise to VM Clocks 101 -- 4.5.3.2 Scheduler-Based Defenses 101 -- 4.5.3.3 Randomized Monitoring Interval 102 -- 4.5.4 Summary 103 -- 4.6 Identifying Compromised Users in Shared Computing Infrastructures 103 -- 4.6.1 Target System and Security Data 104 -- 4.6.1.1 Data and Alerts 105 -- 4.6.1.2 Automating the Analysis of Alerts 106 -- 4.6.2 Overview of the Data 107 -- 4.6.3 Approach 109 -- 4.6.3.1 The Model: Bayesian Network 109 -- 4.6.3.2 Training of the Bayesian Network 110 -- 4.6.4 Analysis of the Incidents 112 -- 4.6.4.1 Sample Incident 112 -- 4.6.4.2 Discussion 113 -- 4.6.5 Supporting Decisions with the Bayesian Network Approach 114 -- 4.6.5.1 Analysis of the Incidents 114 -- 4.6.5.2 Analysis of the Borderline Cases 116 -- 4.6.6 Conclusion 118 -- 4.7 Integrating Attribute-Based Policies into Role-Based Access Control 118 -- 4.7.1 Framework Description 119 -- 4.7.2 Aboveground Level: Tables 119 -- 4.7.2.1 Environment 120.
4.7.2.2 User-Role Assignments 120 -- 4.7.2.3 Role-Permission Assignments 121 -- 4.7.3 Underground Level: Policies 121 -- 4.7.3.1 Role-Permission Assignment Policy 122 -- 4.7.3.2 User-Role Assignment Policy 123 -- 4.7.4 Case Study: Large-Scale ICS 123 -- 4.7.4.1 RBAC Model-Building Process 124 -- 4.7.4.2 Discussion of Case Study 127 -- 4.7.5 Concluding Remarks 128 -- 4.8 The Future 128 -- 5 Scalability, Workloads, and Performance: Replication, Popularity, Modeling, and Geo-Distributed File Stores 133 /Roy H. Campbell, Shadi A. Noghabi, and Cristina L. Abad -- 5.1 Introduction 133 -- 5.2 Vision: Using Cloud Technology in Missions 134 -- 5.3 State of the Art 136 -- 5.4 Data Replication in a Cloud File System 137 -- 5.4.1 MapReduce Clusters 138 -- 5.4.1.1 File Popularity, Temporal Locality, and Arrival Patterns 142 -- 5.4.1.2 Synthetic Workloads for Big Data 144 -- 5.4.2 Related Work 147 -- 5.4.3 Contribution from Our Approach to Generating Big Data Request Streams Using Clustered Renewal Processes 149 -- 5.4.3.1 Scalable Geo-Distributed Storage 149 -- 5.4.4 Related Work 151 -- 5.4.5 Summary of Ambry 152 -- 5.5 Summary 153 -- 5.6 The Future 153 -- 6 Resource Management: Performance Assuredness in Distributed Cloud Computing via Online Reconfigurations 160 /Mainak Ghosh, Le Xu, and Indranil Gupta -- 6.1 Introduction 161 -- 6.2 Vision: Using Cloud Technology in Missions 163 -- 6.3 State of the Art 164 -- 6.3.1 State of the Art: Reconfigurations in Sharded Databases/Storage 164 -- 6.3.1.1 Database Reconfigurations 164 -- 6.3.1.2 Live Migration 164 -- 6.3.1.3 Network Flow Scheduling 164 -- 6.3.2 State of the Art: Scale-Out/Scale-In in Distributed Stream Processing Systems 165 -- 6.3.2.1 Real-Time Reconfigurations 165 -- 6.3.2.2 Live Migration 165 -- 6.3.2.3 Real-Time Elasticity 165 -- 6.3.3 State of the Art: Scale-Out/Scale-In in Distributed Graph Processing Systems 166 -- 6.3.3.1 Data Centers 166 -- 6.3.3.2 Cloud and Storage Systems 166 -- 6.3.3.3 Data Processing Frameworks 166.
6.3.3.4 Partitioning in Graph Processing 166 -- 6.3.3.5 Dynamic Repartitioning in Graph Processing 167 -- 6.3.4 State of the Art: Priorities and Deadlines in Batch Processing Systems 167 -- 6.3.4.1 OS Mechanisms 167 -- 6.3.4.2 Preemption 167 -- 6.3.4.3 Real-Time Scheduling 168 -- 6.3.4.4 Fairness 168 -- 6.3.4.5 Cluster Management with SLOs 168 -- 6.4 Reconfigurations in NoSQL and Key-Value Storage/Databases 169 -- 6.4.1 Motivation 169 -- 6.4.2 Morphus: Reconfigurations in Sharded Databases/Storage 170 -- 6.4.2.1 Assumptions 170 -- 6.4.2.2 MongoDB System Model 170 -- 6.4.2.3 Reconfiguration Phases in Morphus 171 -- 6.4.2.4 Algorithms for Efficient Shard Key Reconfigurations 172 -- 6.4.2.5 Network Awareness 175 -- 6.4.2.6 Evaluation 175 -- 6.4.3 Parqua: Reconfigurations in Distributed Key-Value Stores 179 -- 6.4.3.1 System Model 180 -- 6.4.3.2 System Design and Implementation 181 -- 6.4.3.3 Experimental Evaluation 183 -- 6.5 Scale-Out and Scale-In Operations 185 -- 6.5.1 Stela: Scale-Out/Scale-In in Distributed Stream Processing Systems 186 -- 6.5.1.1 Motivation 186 -- 6.5.1.2 Data Stream Processing Model and Assumptions 187 -- 6.5.1.3 Stela: Scale-Out Overview 187 -- 6.5.1.4 Effective Throughput Percentage (ETP) 188 -- 6.5.1.5 Iterative Assignment and Intuition 190 -- 6.5.1.6 Stela: Scale-In 191 -- 6.5.1.7 Core Architecture 191 -- 6.5.1.8 Evaluation 193 -- 6.5.1.9 Experimental Setup 193 -- 6.5.1.10 Yahoo! Storm Topologies and Network Monitoring Topology 193 -- 6.5.1.11 Convergence Time 195 -- 6.5.1.12 Scale-In Experiments 196 -- 6.5.2 Scale-Out/Scale-In in Distributed Graph Processing Systems 197 -- 6.5.2.1 Motivation 197 -- 6.5.2.2 What to Migrate, and How? 199 -- 6.5.2.3 When to Migrate? 201 -- 6.5.2.4 Evaluation 203 -- 6.6 Priorities and Deadlines in Batch Processing Systems 204 -- 6.6.1 Natjam: Supporting Priorities and Deadlines in Hadoop 204 -- 6.6.1.1 Motivation 204 -- 6.6.1.2 Eviction Policies for a Dual-Priority Setting 206 -- 6.6.1.3 Natjam Architecture 209.
6.6.1.4 Natjam-R: Deadline-Based Eviction 215 -- 6.6.1.5 Microbenchmarks 216 -- 6.6.1.6 Natjam-R Evaluation 221 -- 6.7 Summary 223 -- 6.8 The Future 224 -- 7 Theoretical Considerations: Inferring and Enforcing Use Patterns for Mobile Cloud Assurance 237 /Gul Agha, Minas Charalambides, Kirill Mechitov, Karl Palmskog,Atul Sandur, and Reza Shiftehfar -- 7.1 Introduction 237 -- 7.2 Vision 239 -- 7.3 State of the Art 240 -- 7.3.1 Code Offloading 241 -- 7.3.2 Coordination Constraints 241 -- 7.3.3 Session Types 242 -- 7.4 Code Offloading and the IMCM Framework 243 -- 7.4.1 IMCM Framework: Overview 244 -- 7.4.2 Cloud Application and Infrastructure Models 244 -- 7.4.3 Cloud Application Model 245 -- 7.4.4 Defining Privacy for Mobile Hybrid Cloud Applications 247 -- 7.4.5 A Face Recognition Application 247 -- 7.4.6 The Design of an Authorization System 249 -- 7.4.7 Mobile Hybrid Cloud Authorization Language 250 -- 7.4.7.1 Grouping, Selection, and Binding 252 -- 7.4.7.2 Policy Description 252 -- 7.4.7.3 Policy Evaluation 253 -- 7.4.8 Performance- and Energy-Usage-Based Code Offloading 254 -- 7.4.8.1 Offloading for Sequential Execution on a Single Server 254 -- 7.4.8.2 Offloading for Parallel Execution on Hybrid Clouds 255 -- 7.4.8.3 Maximizing Performance 255 -- 7.4.8.4 Minimizing Energy Consumption 256 -- 7.4.8.5 Energy Monitoring 257 -- 7.4.8.6 Security Policies and Energy Monitoring 258 -- 7.5 Coordinating Actors 259 -- 7.5.1 Expressing Coordination 259 -- 7.5.1.1 Synchronizers 260 -- 7.5.1.2 Security Issues in Synchronizers 260 -- 7.6 Session Types 264 -- 7.6.1 Session Types for Actors 265 -- 7.6.1.1 Example: Sliding Window Protocol 265 -- 7.6.2 Global Types 266 -- 7.6.3 Programming Language 268 -- 7.6.4 Local Types and Type Checking 269 -- 7.6.5 Realization of Global Types 270 -- 7.7 The Future 271 -- Acknowledgments 272 -- 8 Certifications Past and Future: A Future Model for Assigning Certifications that Incorporate Lessons Learned from Past Practices 277 /Masooda Bashir, Carlo Di Giulio, and Charles A. Kamhoua.
8.1 Introduction 277 -- 8.1.1 What Is a Standard? 279 -- 8.1.2 Standards and Cloud Computing 281 -- 8.2 Vision: Using Cloud Technology in Missions 283 -- 8.3 State of the Art 284 -- 8.3.1 The Federal Risk Authorization Management Program 286 -- 8.3.2 SOC Reports and TSPC 288 -- 8.3.3 ISO/IEC 27001 291 -- 8.3.4 Main Differences among the Standards 292 -- 8.3.5 Other Existing Frameworks 293 -- 8.3.5.1 PCI-DSS 293 -- 8.3.5.2 C5 294 -- 8.3.5.3 STAR 294 -- 8.3.6 What Protections Do Standards Offer against Vulnerabilities in the Cloud? 294 -- 8.4 Comparison among Standards 296 -- 8.4.1 Strategy for Comparing Standards 298 -- 8.4.2 Patterns, Anomalies, and Discoveries 299 -- 8.5 The Future 302 -- 8.5.1 Current Challenges 304 -- 8.5.2 Opportunities 305 -- 9 Summary and Future Work 312 /Roy H. Campbell -- 9.1 Survivability 312 -- 9.2 Risks and Benefits 313 -- 9.3 Detection and Security 314 -- 9.4 Scalability, Workloads, and Performance 316 -- 9.5 Resource Management 319 -- 9.6 Theoretical Considerations: Inferring and Enforcing Use Patterns for Mobile Cloud Assurance 321 -- 9.7 Certifications 322 -- Index 327.
Record Nr. UNINA-9910830412903321
Campbell Roy  
Hoboken, New Jersey : , : IEEE Computer Society, Inc./Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Blockchain for distributed systems security / / edited by Sachin S. Shetty, Charles A. Kamhoua, Laurent L. Njilla
Blockchain for distributed systems security / / edited by Sachin S. Shetty, Charles A. Kamhoua, Laurent L. Njilla
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley-IEEE, , [2019]
Descrizione fisica 1 online resource (347 pages) : illustrations
Disciplina 005.824
Soggetto topico Blockchains (Databases)
Internet auctions - Security measures
ISBN 1-119-51958-6
1-119-51962-4
1-119-51959-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword xiii -- Preface xv -- List of Contributors xix -- Part I Introduction to Blockchain 1 -- 1 Introduction 3 /Sachin S. Shetty, Laurent Njilla, and Charles A. Kamhoua -- 1.1 Blockchain Overview 3 -- 1.1.1 Blockchain Building Blocks 5 -- 1.1.2 Blockchain Commercial Use Cases 6 -- 1.1.3 Blockchain Military Cyber Operations Use Cases 11 -- 1.1.4 Blockchain Challenges 13 -- 1.2 Overview of the Book 16 -- 1.2.1 Chapter 2: Distributed Consensus Protocols and Algorithms 16 -- 1.2.2 Chapter 3: Overview of Attack Surfaces in Blockchain 17 -- 1.2.3 Chapter 4: Data Provenance in Cloud Storage with Blockchain 17 -- 1.2.4 Chapter 5: Blockchain-based Solution to Automotive Security and Privacy 18 -- 1.2.5 Chapter 6: Blockchain-based Dynamic Key Management for IoT-Transportation Security Protection 19 -- 1.2.6 Chapter 7: Blockchain-enabled Information Sharing Framework for Cybersecurity 19 -- 1.2.7 Chapter 8: Blockcloud Security Analysis 20 -- 1.2.8 Chapter 9: Security and Privacy of Permissioned and Permissionless Blockchain 20 -- 1.2.9 Chapter 10: Shocking Public Blockchains’ Memory with Unconfirmed Transactions-New DDoS Attacks and Countermeasures 21 -- 1.2.10 Chapter 11: Preventing Digital Currency Miners From Launching Attacks Against Mining Pools by a Reputation-Based Paradigm 21 -- 1.2.11 Chapter 12: Private Blockchain Configurations for Improved IoT Security 22 -- 1.2.12 Chapter 13: Blockchain Evaluation Platform 22 -- References 23 -- 2 Distributed Consensus Protocols and Algorithms 25 /Yang Xiao, Ning Zhang, Jin Li, Wenjing Lou, and Y. Thomas Hou -- 2.1 Introduction 25 -- 2.2 Fault-tolerant Consensus in a Distributed System 26 -- 2.2.1 The System Model 26 -- 2.2.2 BFT Consensus 28 -- 2.2.3 The OM Algorithm 29 -- 2.2.4 Practical Consensus Protocols in Distributed Computing 30 -- 2.3 The Nakamoto Consensus 37 -- 2.3.1 The Consensus Problem 38 -- 2.3.2 Network Model 38 -- 2.3.3 The Consensus Protocol 39 -- 2.4 Emerging Blockchain Consensus Algorithms 40 -- 2.4.1 Proof of Stake 41.
2.4.2 BFT-based Consensus 42 -- 2.4.3 Proof of Elapsed Time (PoET) 44 -- 2.4.4 Ripple 45 -- 2.5 Evaluation and Comparison 47 -- 2.6 Summary 47 -- Acknowledgment 49 -- References 49 -- 3 Overview of Attack Surfaces in Blockchain 51 /Muhammad Saad, Jeffrey Spaulding, Laurent Njilla, Charles A. Kamhoua, DaeHun Nyang, and Aziz Mohaisen -- 3.1 Introduction 51 -- 3.2 Overview of Blockchain and its Operations 53 -- 3.3 Blockchain Attacks 54 -- 3.3.1 Blockchain Fork 54 -- 3.3.2 Stale Blocks and Orphaned Blocks 54 -- 3.3.3 Countering Blockchain Structure Attacks 55 -- 3.4 Blockchain’s Peer-to-Peer System 55 -- 3.4.1 Selfish Mining 56 -- 3.4.2 The 51% Attack 57 -- 3.4.3 DNS Attacks 57 -- 3.4.4 DDoS Attacks 58 -- 3.4.5 Consensus Delay 59 -- 3.4.6 Countering Peer-to-Peer Attacks 59 -- 3.5 Application Oriented Attacks 60 -- 3.5.1 Blockchain Ingestion 60 -- 3.5.2 Double Spending 60 -- 3.5.3 Wallet Theft 61 -- 3.5.4 Countering Application Oriented Attacks 61 -- 3.6 Related Work 61 -- 3.7 Conclusion and Future Work 62 -- References 62 -- Part II Blockchain Solutions for Distributed System Security 67 -- 4 ProvChain: Blockchain-based Cloud Data Provenance 69 /Xueping Liang, Sachin S. Shetty, Deepak Tosh, Laurent Njilla, Charles A. Kamhoua, and Kevin Kwiat -- 4.1 Introduction 69 -- 4.2 Background and Related Work 70 -- 4.2.1 Data Provenance 70 -- 4.2.2 Data Provenance in the Cloud 71 -- 4.2.3 Blockchain 73 -- 4.2.4 Blockchain and Data Provenance 74 -- 4.3 ProvChain Architecture 75 -- 4.3.1 Architecture Overview 76 -- 4.3.2 Preliminaries and Concepts 77 -- 4.3.3 Threat Model 78 -- 4.3.4 Key Establishment 78 -- 4.4 ProvChain Implementation 79 -- 4.4.1 Provenance Data Collection and Storage 80 -- 4.4.2 Provenance Data Validation 83 -- 4.5 Evaluation 85 -- 4.5.1 Summary of ProvChain’s Capabilities 85 -- 4.5.2 Performance and Overhead 86 -- 4.6 Conclusions and Future Work 90 -- Acknowledgment 91 -- References 92 -- 5 A Blockchain-based Solution to Automotive Security and Privacy 95 /Ali Dorri, Marco Steger, Salil S. Kanhere, and Raja Jurdak.
5.1 Introduction 95 -- 5.2 An Introduction to Blockchain 98 -- 5.3 The Proposed Framework 101 -- 5.4 Applications 103 -- 5.4.1 Remote Software Updates 103 -- 5.4.2 Insurance 105 -- 5.4.3 Electric Vehicles and Smart Charging Services 105 -- 5.4.4 Car-sharing Services 106 -- 5.4.5 Supply Chain 106 -- 5.4.6 Liability 107 -- 5.5 Evaluation and Discussion 108 -- 5.5.1 Security and Privacy Analysis 108 -- 5.5.2 Performance Evaluation 109 -- 5.6 Related Works 112 -- 5.7 Conclusion 113 -- References 114 -- 6 Blockchain-based Dynamic Key Management for IoT-Transportation Security Protection 117 /Ao Lei, Yue Cao, Shihan Bao, Philip Asuquom, Haitham Cruickshank, and Zhili Sun -- 6.1 Introduction 117 -- 6.2 Use Case 119 -- 6.2.1 Message Handover in VCS 120 -- 6.3 Blockchain-based Dynamic Key Management Scheme 124 -- 6.4 Dynamic Transaction Collection Algorithm 125 -- 6.4.1 Transaction Format 125 -- 6.4.2 Block Format 127 -- 6.5 Time Composition 128 -- 6.5.1 Dynamic Transaction Collection Algorithm 129 -- 6.6 Performance Evaluation 130 -- 6.6.1 Experimental Assumptions and Setup 130 -- 6.6.2 Processing Time of Cryptographic Schemes 132 -- 6.6.3 Handover Time 133 -- 6.6.4 Performance of the Dynamic Transaction Collection Algorithm 135 -- 6.7 Conclusion and Future Work 138 -- References 140 -- 7 Blockchain-enabled Information Sharing Framework for Cybersecurity 143 /Abdulhamid Adebayo, Danda B. Rawat, Laurent Njilla, and Charles A. Kamhoua -- 7.1 Introduction 143 -- 7.2 The BIS Framework 145 -- 7.3 Transactions on BIS 146 -- 7.4 Cyberattack Detection and Information Sharing 147 -- 7.5 Cross-group Attack Game in Blockchain-based BIS Framework: One-way Attack 149 -- 7.6 Cross-group Attack Game in Blockchain-based BIS Framework: Two-way Attack 151 -- 7.7 Stackelberg Game for Cyberattack and Defense Analysis 152 -- 7.8 Conclusion 156 -- References 157 -- Part III Blockchain Security 159 -- 8 Blockcloud Security Analysis 161 /Deepak Tosh, Sachin S. Shetty, Xueping Liang, Laurent Njilla, Charles A. Kamhoua, and Kevin Kwiat.
8.1 Introduction 161 -- 8.2 Blockchain Consensus Mechanisms 163 -- 8.2.1 Proof-of-Work (PoW) Consensus 164 -- 8.2.2 Proof-of-Stake (PoS) Consensus 165 -- 8.2.3 Proof-of-Activity (PoA) Consensus 167 -- 8.2.4 Practical Byzantine Fault Tolerance (PBFT) Consensus 168 -- 8.2.5 Proof-of-Elapsed-Time (PoET) Consensus 169 -- 8.2.6 Proof-of-Luck (PoL) Consensus 170 -- 8.2.7 Proof-of-Space (PoSpace) Consensus 170 -- 8.3 Blockchain Cloud and Associated Vulnerabilities 171 -- 8.3.1 Blockchain and Cloud Security 171 -- 8.3.2 Blockchain Cloud Vulnerabilities 174 -- 8.4 System Model 179 -- 8.5 Augmenting with Extra Hash Power 180 -- 8.6 Disruptive Attack Strategy Analysis 181 -- 8.6.1 Proportional Reward 181 -- 8.6.2 Pay-per-last N-shares (PPLNS) Reward 184 -- 8.7 Simulation Results and Discussion 187 -- 8.8 Conclusions and Future Directions 188 -- Acknowledgment 190 -- References 190 -- 9 Permissioned and Permissionless Blockchains 193 /Andrew Miller -- 9.1 Introduction 193 -- 9.2 On Choosing Your Peers Wisely 194 -- 9.3 Committee Election Mechanisms 196 -- 9.4 Privacy in Permissioned and Permissionless Blockchains 199 -- 9.5 Conclusion 201 -- References 202 -- 10 Shocking Blockchain’s Memory with Unconfirmed Transactions: New DDoS Attacks and Countermeasures 205 /Muhammad Saad, Laurent Njilla, Charles A. Kamhoua, Kevin Kwiat, and Aziz Mohaisen -- 10.1 Introduction 205 -- 10.2 Related Work 207 -- 10.3 An Overview of Blockchain and Lifecycle 208 -- 10.3.1 DDoS Attack on Mempools 210 -- 10.3.2 Data Collection for Evaluation 210 -- 10.4 Threat Model 211 -- 10.5 Attack Procedure 212 -- 10.5.1 The Distribution Phase 214 -- 10.5.2 The Attack Phase 214 -- 10.5.3 Attack Cost 214 -- 10.6 Countering the Mempool Attack 215 -- 10.6.1 Fee-based Mempool Design 216 -- 10.6.2 Age-based Countermeasures 221 -- 10.7 Experiment and Results 224 -- 10.8 Conclusion 227 -- References 227 -- 11 Preventing Digital Currency Miners from Launching Attacks Against Mining Pools Using a Reputation-based Paradigm 233 /Mehrdad Nojoumian, Arash Golchubian, Laurent Njilla, Kevin Kwiat, and Charles A. Kamhoua.
11.1 Introduction 233 -- 11.2 Preliminaries 234 -- 11.2.1 Digital Currencies: Terminologies and Mechanics 234 -- 11.2.2 Game Theory: Basic Notions and Definitions 235 -- 11.3 Literature Review 236 -- 11.4 Reputation-based Mining Model and Setting 238 -- 11.5 Mining in a Reputation-based Model 240 -- 11.5.1 Prevention of the Re-entry Attack 240 -- 11.5.2 Technical Discussion on Detection Mechanisms 241 -- 11.5.3 Colluding Miner’s Dilemma 243 -- 11.5.4 Repeated Mining Game 244 -- 11.5.5 Colluding Miners’ Preferences 245 -- 11.5.6 Colluding Miners’ Utilities 245 -- 11.6 Evaluation of Our Model Using Game-theoretical Analyses 246 -- 11.7 Concluding Remarks 248 -- Acknowledgment 249 -- References 249 -- Part IV Blockchain Implementation 253 -- 12 Private Blockchain Configurations for Improved IoT Security 255 /Adriaan Larmuseau and Devu Manikantan Shila -- 12.1 Introduction 255 -- 12.2 Blockchain-enabled Gateway 257 -- 12.2.1 Advantages 257 -- 12.2.2 Limitations 258 -- 12.2.3 Private Ethereum Gateways for Access Control 259 -- 12.2.4 Evaluation 262 -- 12.3 Blockchain-enabled Smart End Devices 263 -- 12.3.1 Advantages 263 -- 12.3.2 Limitations 264 -- 12.3.3 Private Hyperledger Blockchain-enabled Smart Sensor Devices 264 -- 12.3.4 Evaluation 269 -- 12.4 Related Work 270 -- 12.5 Conclusion 271 -- References 271 -- 13 Blockchain Evaluation Platform 275 /Peter Foytik and Sachin S. Shetty -- 13.1 Introduction 275 -- 13.1.1 Architecture 276 -- 13.1.2 Distributed Ledger 276 -- 13.1.3 Participating Nodes 277 -- 13.1.4 Communication 277 -- 13.1.5 Consensus 278 -- 13.2 Hyperledger Fabric 279 -- 13.2.1 Node Types 279 -- 13.2.2 Docker 280 -- 13.2.3 Hyperledger Fabric Example Exercise 281 -- 13.2.4 Running the First Network 281 -- 13.2.5 Running the Kafka Network 286 -- 13.3 Measures of Performance 291 -- 13.3.1 Performance Metrics With the Proof-of-Stake Simulation 293 -- 13.3.2 Performance Measures With the Hyperledger Fabric Example 296 -- 13.4 Simple Blockchain Simulation 300.
13.5 Blockchain Simulation Introduction 303 -- 13.5.1 Methodology 304 -- 13.5.2 Simulation Integration With Live Blockchain 304 -- 13.5.3 Simulation Integration With Simulated Blockchain 306 -- 13.5.4 Verification and Validation 306 -- 13.5.5 Example 307 -- 13.6 Conclusion and Future Work 309 -- References 310 -- 14 Summary and Future Work 311 /Sachin S. Shetty, Laurent Njilla, and Charles A. Kamhoua -- 14.1 Introduction 311 -- 14.2 Blockchain and Cloud Security 312 -- 14.3 Blockchain and IoT Security 312 -- 14.4 Blockchain Security and Privacy 314 -- 14.5 Experimental Testbed and Performance Evaluation 316 -- 14.6 The Future 316 -- Index 319.
Record Nr. UNINA-9910555114003321
Hoboken, New Jersey : , : Wiley-IEEE, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Blockchain for distributed systems security / / edited by Sachin S. Shetty, Charles A. Kamhoua, Laurent L. Njilla
Blockchain for distributed systems security / / edited by Sachin S. Shetty, Charles A. Kamhoua, Laurent L. Njilla
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley-IEEE, , [2019]
Descrizione fisica 1 online resource (347 pages) : illustrations
Disciplina 005.824
Soggetto topico Blockchains (Databases)
Internet auctions - Security measures
ISBN 1-119-51958-6
1-119-51962-4
1-119-51959-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword xiii -- Preface xv -- List of Contributors xix -- Part I Introduction to Blockchain 1 -- 1 Introduction 3 /Sachin S. Shetty, Laurent Njilla, and Charles A. Kamhoua -- 1.1 Blockchain Overview 3 -- 1.1.1 Blockchain Building Blocks 5 -- 1.1.2 Blockchain Commercial Use Cases 6 -- 1.1.3 Blockchain Military Cyber Operations Use Cases 11 -- 1.1.4 Blockchain Challenges 13 -- 1.2 Overview of the Book 16 -- 1.2.1 Chapter 2: Distributed Consensus Protocols and Algorithms 16 -- 1.2.2 Chapter 3: Overview of Attack Surfaces in Blockchain 17 -- 1.2.3 Chapter 4: Data Provenance in Cloud Storage with Blockchain 17 -- 1.2.4 Chapter 5: Blockchain-based Solution to Automotive Security and Privacy 18 -- 1.2.5 Chapter 6: Blockchain-based Dynamic Key Management for IoT-Transportation Security Protection 19 -- 1.2.6 Chapter 7: Blockchain-enabled Information Sharing Framework for Cybersecurity 19 -- 1.2.7 Chapter 8: Blockcloud Security Analysis 20 -- 1.2.8 Chapter 9: Security and Privacy of Permissioned and Permissionless Blockchain 20 -- 1.2.9 Chapter 10: Shocking Public Blockchains’ Memory with Unconfirmed Transactions-New DDoS Attacks and Countermeasures 21 -- 1.2.10 Chapter 11: Preventing Digital Currency Miners From Launching Attacks Against Mining Pools by a Reputation-Based Paradigm 21 -- 1.2.11 Chapter 12: Private Blockchain Configurations for Improved IoT Security 22 -- 1.2.12 Chapter 13: Blockchain Evaluation Platform 22 -- References 23 -- 2 Distributed Consensus Protocols and Algorithms 25 /Yang Xiao, Ning Zhang, Jin Li, Wenjing Lou, and Y. Thomas Hou -- 2.1 Introduction 25 -- 2.2 Fault-tolerant Consensus in a Distributed System 26 -- 2.2.1 The System Model 26 -- 2.2.2 BFT Consensus 28 -- 2.2.3 The OM Algorithm 29 -- 2.2.4 Practical Consensus Protocols in Distributed Computing 30 -- 2.3 The Nakamoto Consensus 37 -- 2.3.1 The Consensus Problem 38 -- 2.3.2 Network Model 38 -- 2.3.3 The Consensus Protocol 39 -- 2.4 Emerging Blockchain Consensus Algorithms 40 -- 2.4.1 Proof of Stake 41.
2.4.2 BFT-based Consensus 42 -- 2.4.3 Proof of Elapsed Time (PoET) 44 -- 2.4.4 Ripple 45 -- 2.5 Evaluation and Comparison 47 -- 2.6 Summary 47 -- Acknowledgment 49 -- References 49 -- 3 Overview of Attack Surfaces in Blockchain 51 /Muhammad Saad, Jeffrey Spaulding, Laurent Njilla, Charles A. Kamhoua, DaeHun Nyang, and Aziz Mohaisen -- 3.1 Introduction 51 -- 3.2 Overview of Blockchain and its Operations 53 -- 3.3 Blockchain Attacks 54 -- 3.3.1 Blockchain Fork 54 -- 3.3.2 Stale Blocks and Orphaned Blocks 54 -- 3.3.3 Countering Blockchain Structure Attacks 55 -- 3.4 Blockchain’s Peer-to-Peer System 55 -- 3.4.1 Selfish Mining 56 -- 3.4.2 The 51% Attack 57 -- 3.4.3 DNS Attacks 57 -- 3.4.4 DDoS Attacks 58 -- 3.4.5 Consensus Delay 59 -- 3.4.6 Countering Peer-to-Peer Attacks 59 -- 3.5 Application Oriented Attacks 60 -- 3.5.1 Blockchain Ingestion 60 -- 3.5.2 Double Spending 60 -- 3.5.3 Wallet Theft 61 -- 3.5.4 Countering Application Oriented Attacks 61 -- 3.6 Related Work 61 -- 3.7 Conclusion and Future Work 62 -- References 62 -- Part II Blockchain Solutions for Distributed System Security 67 -- 4 ProvChain: Blockchain-based Cloud Data Provenance 69 /Xueping Liang, Sachin S. Shetty, Deepak Tosh, Laurent Njilla, Charles A. Kamhoua, and Kevin Kwiat -- 4.1 Introduction 69 -- 4.2 Background and Related Work 70 -- 4.2.1 Data Provenance 70 -- 4.2.2 Data Provenance in the Cloud 71 -- 4.2.3 Blockchain 73 -- 4.2.4 Blockchain and Data Provenance 74 -- 4.3 ProvChain Architecture 75 -- 4.3.1 Architecture Overview 76 -- 4.3.2 Preliminaries and Concepts 77 -- 4.3.3 Threat Model 78 -- 4.3.4 Key Establishment 78 -- 4.4 ProvChain Implementation 79 -- 4.4.1 Provenance Data Collection and Storage 80 -- 4.4.2 Provenance Data Validation 83 -- 4.5 Evaluation 85 -- 4.5.1 Summary of ProvChain’s Capabilities 85 -- 4.5.2 Performance and Overhead 86 -- 4.6 Conclusions and Future Work 90 -- Acknowledgment 91 -- References 92 -- 5 A Blockchain-based Solution to Automotive Security and Privacy 95 /Ali Dorri, Marco Steger, Salil S. Kanhere, and Raja Jurdak.
5.1 Introduction 95 -- 5.2 An Introduction to Blockchain 98 -- 5.3 The Proposed Framework 101 -- 5.4 Applications 103 -- 5.4.1 Remote Software Updates 103 -- 5.4.2 Insurance 105 -- 5.4.3 Electric Vehicles and Smart Charging Services 105 -- 5.4.4 Car-sharing Services 106 -- 5.4.5 Supply Chain 106 -- 5.4.6 Liability 107 -- 5.5 Evaluation and Discussion 108 -- 5.5.1 Security and Privacy Analysis 108 -- 5.5.2 Performance Evaluation 109 -- 5.6 Related Works 112 -- 5.7 Conclusion 113 -- References 114 -- 6 Blockchain-based Dynamic Key Management for IoT-Transportation Security Protection 117 /Ao Lei, Yue Cao, Shihan Bao, Philip Asuquom, Haitham Cruickshank, and Zhili Sun -- 6.1 Introduction 117 -- 6.2 Use Case 119 -- 6.2.1 Message Handover in VCS 120 -- 6.3 Blockchain-based Dynamic Key Management Scheme 124 -- 6.4 Dynamic Transaction Collection Algorithm 125 -- 6.4.1 Transaction Format 125 -- 6.4.2 Block Format 127 -- 6.5 Time Composition 128 -- 6.5.1 Dynamic Transaction Collection Algorithm 129 -- 6.6 Performance Evaluation 130 -- 6.6.1 Experimental Assumptions and Setup 130 -- 6.6.2 Processing Time of Cryptographic Schemes 132 -- 6.6.3 Handover Time 133 -- 6.6.4 Performance of the Dynamic Transaction Collection Algorithm 135 -- 6.7 Conclusion and Future Work 138 -- References 140 -- 7 Blockchain-enabled Information Sharing Framework for Cybersecurity 143 /Abdulhamid Adebayo, Danda B. Rawat, Laurent Njilla, and Charles A. Kamhoua -- 7.1 Introduction 143 -- 7.2 The BIS Framework 145 -- 7.3 Transactions on BIS 146 -- 7.4 Cyberattack Detection and Information Sharing 147 -- 7.5 Cross-group Attack Game in Blockchain-based BIS Framework: One-way Attack 149 -- 7.6 Cross-group Attack Game in Blockchain-based BIS Framework: Two-way Attack 151 -- 7.7 Stackelberg Game for Cyberattack and Defense Analysis 152 -- 7.8 Conclusion 156 -- References 157 -- Part III Blockchain Security 159 -- 8 Blockcloud Security Analysis 161 /Deepak Tosh, Sachin S. Shetty, Xueping Liang, Laurent Njilla, Charles A. Kamhoua, and Kevin Kwiat.
8.1 Introduction 161 -- 8.2 Blockchain Consensus Mechanisms 163 -- 8.2.1 Proof-of-Work (PoW) Consensus 164 -- 8.2.2 Proof-of-Stake (PoS) Consensus 165 -- 8.2.3 Proof-of-Activity (PoA) Consensus 167 -- 8.2.4 Practical Byzantine Fault Tolerance (PBFT) Consensus 168 -- 8.2.5 Proof-of-Elapsed-Time (PoET) Consensus 169 -- 8.2.6 Proof-of-Luck (PoL) Consensus 170 -- 8.2.7 Proof-of-Space (PoSpace) Consensus 170 -- 8.3 Blockchain Cloud and Associated Vulnerabilities 171 -- 8.3.1 Blockchain and Cloud Security 171 -- 8.3.2 Blockchain Cloud Vulnerabilities 174 -- 8.4 System Model 179 -- 8.5 Augmenting with Extra Hash Power 180 -- 8.6 Disruptive Attack Strategy Analysis 181 -- 8.6.1 Proportional Reward 181 -- 8.6.2 Pay-per-last N-shares (PPLNS) Reward 184 -- 8.7 Simulation Results and Discussion 187 -- 8.8 Conclusions and Future Directions 188 -- Acknowledgment 190 -- References 190 -- 9 Permissioned and Permissionless Blockchains 193 /Andrew Miller -- 9.1 Introduction 193 -- 9.2 On Choosing Your Peers Wisely 194 -- 9.3 Committee Election Mechanisms 196 -- 9.4 Privacy in Permissioned and Permissionless Blockchains 199 -- 9.5 Conclusion 201 -- References 202 -- 10 Shocking Blockchain’s Memory with Unconfirmed Transactions: New DDoS Attacks and Countermeasures 205 /Muhammad Saad, Laurent Njilla, Charles A. Kamhoua, Kevin Kwiat, and Aziz Mohaisen -- 10.1 Introduction 205 -- 10.2 Related Work 207 -- 10.3 An Overview of Blockchain and Lifecycle 208 -- 10.3.1 DDoS Attack on Mempools 210 -- 10.3.2 Data Collection for Evaluation 210 -- 10.4 Threat Model 211 -- 10.5 Attack Procedure 212 -- 10.5.1 The Distribution Phase 214 -- 10.5.2 The Attack Phase 214 -- 10.5.3 Attack Cost 214 -- 10.6 Countering the Mempool Attack 215 -- 10.6.1 Fee-based Mempool Design 216 -- 10.6.2 Age-based Countermeasures 221 -- 10.7 Experiment and Results 224 -- 10.8 Conclusion 227 -- References 227 -- 11 Preventing Digital Currency Miners from Launching Attacks Against Mining Pools Using a Reputation-based Paradigm 233 /Mehrdad Nojoumian, Arash Golchubian, Laurent Njilla, Kevin Kwiat, and Charles A. Kamhoua.
11.1 Introduction 233 -- 11.2 Preliminaries 234 -- 11.2.1 Digital Currencies: Terminologies and Mechanics 234 -- 11.2.2 Game Theory: Basic Notions and Definitions 235 -- 11.3 Literature Review 236 -- 11.4 Reputation-based Mining Model and Setting 238 -- 11.5 Mining in a Reputation-based Model 240 -- 11.5.1 Prevention of the Re-entry Attack 240 -- 11.5.2 Technical Discussion on Detection Mechanisms 241 -- 11.5.3 Colluding Miner’s Dilemma 243 -- 11.5.4 Repeated Mining Game 244 -- 11.5.5 Colluding Miners’ Preferences 245 -- 11.5.6 Colluding Miners’ Utilities 245 -- 11.6 Evaluation of Our Model Using Game-theoretical Analyses 246 -- 11.7 Concluding Remarks 248 -- Acknowledgment 249 -- References 249 -- Part IV Blockchain Implementation 253 -- 12 Private Blockchain Configurations for Improved IoT Security 255 /Adriaan Larmuseau and Devu Manikantan Shila -- 12.1 Introduction 255 -- 12.2 Blockchain-enabled Gateway 257 -- 12.2.1 Advantages 257 -- 12.2.2 Limitations 258 -- 12.2.3 Private Ethereum Gateways for Access Control 259 -- 12.2.4 Evaluation 262 -- 12.3 Blockchain-enabled Smart End Devices 263 -- 12.3.1 Advantages 263 -- 12.3.2 Limitations 264 -- 12.3.3 Private Hyperledger Blockchain-enabled Smart Sensor Devices 264 -- 12.3.4 Evaluation 269 -- 12.4 Related Work 270 -- 12.5 Conclusion 271 -- References 271 -- 13 Blockchain Evaluation Platform 275 /Peter Foytik and Sachin S. Shetty -- 13.1 Introduction 275 -- 13.1.1 Architecture 276 -- 13.1.2 Distributed Ledger 276 -- 13.1.3 Participating Nodes 277 -- 13.1.4 Communication 277 -- 13.1.5 Consensus 278 -- 13.2 Hyperledger Fabric 279 -- 13.2.1 Node Types 279 -- 13.2.2 Docker 280 -- 13.2.3 Hyperledger Fabric Example Exercise 281 -- 13.2.4 Running the First Network 281 -- 13.2.5 Running the Kafka Network 286 -- 13.3 Measures of Performance 291 -- 13.3.1 Performance Metrics With the Proof-of-Stake Simulation 293 -- 13.3.2 Performance Measures With the Hyperledger Fabric Example 296 -- 13.4 Simple Blockchain Simulation 300.
13.5 Blockchain Simulation Introduction 303 -- 13.5.1 Methodology 304 -- 13.5.2 Simulation Integration With Live Blockchain 304 -- 13.5.3 Simulation Integration With Simulated Blockchain 306 -- 13.5.4 Verification and Validation 306 -- 13.5.5 Example 307 -- 13.6 Conclusion and Future Work 309 -- References 310 -- 14 Summary and Future Work 311 /Sachin S. Shetty, Laurent Njilla, and Charles A. Kamhoua -- 14.1 Introduction 311 -- 14.2 Blockchain and Cloud Security 312 -- 14.3 Blockchain and IoT Security 312 -- 14.4 Blockchain Security and Privacy 314 -- 14.5 Experimental Testbed and Performance Evaluation 316 -- 14.6 The Future 316 -- Index 319.
Record Nr. UNINA-9910676564003321
Hoboken, New Jersey : , : Wiley-IEEE, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Game theory and machine learning for cyber security / / editors, Charles A. Kamhoua [et al.]
Game theory and machine learning for cyber security / / editors, Charles A. Kamhoua [et al.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc., , [2021]
Descrizione fisica 1 online resource (547 pages)
Disciplina 005.8
Soggetto topico Computer networks - Security measures
Game theory
Machine learning
ISBN 1-119-72394-9
1-119-72395-7
1-119-72391-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- Editor Biographies -- Contributors -- Foreword -- Preface -- Chapter 1 Introduction -- 1.1 Artificial Intelligence and Cybersecurity -- 1.1.1 Game Theory for Cybersecurity -- 1.1.2 Machine Learning for Cybersecurity -- 1.2 Overview -- References -- Part I Game Theory for Cyber Deception -- Chapter 2 Introduction to Game Theory -- 2.1 Overview -- 2.2 Example Two‐Player Zero‐Sum Games -- 2.3 Normal‐Form Games -- 2.3.1 Solution Concepts -- 2.4 Extensive‐Form Games -- 2.4.1 Solution Concepts -- 2.5 Stackelberg Game -- 2.5.1 Solution Concept -- 2.5.2 Stackelberg Security Games -- 2.5.3 Applications in Cybersecurity -- 2.6 Repeated Games -- 2.6.1 Solution Concepts -- 2.6.2 Applications in Cybersecurity -- 2.7 Bayesian Games -- 2.7.1 Solution Concepts -- 2.7.2 Applications in Cybersecurity -- 2.8 Stochastic Games -- 2.8.1 Solution Concepts -- 2.8.2 Applications in Cybersecurity -- References -- Chapter 3 Scalable Algorithms for Identifying Stealthy Attackers in a Game‐Theoretic Framework Using Deception -- 3.1 Introduction -- 3.2 Background -- 3.3 Case Studies -- 3.3.1 Case Study 1: Attackers with Same Exploits but Different Goals -- 3.3.2 Case Study 2: Attackers with Shared Exploits and Different Goals -- 3.3.3 Case Study 3: Attackers with Shared Exploits but Same Goals -- 3.4 Game Model -- 3.5 Defender Decision Making -- 3.6 Attacker Decision Making -- 3.7 Simulation Results -- 3.8 Scalability -- 3.8.1 Heuristics -- 3.9 Evaluation of Heuristics -- 3.10 Conclusions and Future Direction -- References -- Chapter 4 Honeypot Allocation Games over Attack Graphs for Cyber Deception -- 4.1 Introduction -- 4.2 System and Game Model -- 4.2.1 Attack Graph -- 4.2.2 General Game Formulation -- 4.2.2.1 Defender Action -- 4.2.2.2 Attacker Action -- 4.2.3 Reward Function -- 4.2.4 Mixed Strategy.
4.2.5 System Parameters -- 4.3 Allocating ℓ Honeypots Model -- 4.3.1 The Algorithm -- 4.4 Dynamic Honeypot Allocation -- 4.4.1 Mixed Strategy, State Evolution, and Objective Function -- 4.4.2 Q‐Minmax Algorithm -- 4.5 Numerical Results -- 4.6 Conclusion and Future Work -- Acknowledgment -- References -- Chapter 5 Evaluating Adaptive Deception Strategies for Cyber Defense with Human Adversaries -- 5.1 Introduction -- 5.1.1 HoneyGame: An Abstract Interactive Game to Study Deceptive Cyber Defense -- 5.2 An Ecology of Defense Algorithms -- 5.2.1 Static Pure Defender -- 5.2.2 Static Equilibrium Defender -- 5.2.3 Learning with Linear Rewards (LLR) -- 5.2.4 Best Response with Thompson sampling (BR‐TS) -- 5.2.5 Probabilistic Best Response with Thompson Sampling (PBR‐TS) -- 5.2.6 Follow the Regularized Leader (FTRL) -- 5.3 Experiments -- 5.3.1 Measures -- 5.4 Experiment 1 -- 5.4.1 Participants -- 5.4.2 Procedure -- 5.4.3 Results -- 5.4.3.1 Average Rewards -- 5.4.3.2 Attacks on Honeypots -- 5.4.3.3 Switching Behavior -- 5.4.3.4 Attack Distribution -- 5.5 Experiment 2 -- 5.5.1 Participants -- 5.5.2 Results -- 5.5.2.1 Average Rewards -- 5.5.2.2 Attacks on Honeypots -- 5.5.2.3 Switching Behavior -- 5.5.2.4 Attack Distribution -- 5.6 Towards Adaptive and Personalized Defense -- 5.7 Conclusions -- Acknowledgements -- References -- Chapter 6 A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception -- 6.1 Introduction -- 6.2 Attack‐Defend Games on Graph -- 6.2.1 Game Arena -- 6.2.2 Specifying the Security Properties in LTL -- 6.3 Hypergames on Graphs -- 6.4 Synthesis of Provably Secure Defense Strategies Using Hypergames on Graphs -- 6.4.1 Synthesis of Reactive Defense Strategies -- 6.4.2 Synthesis of Reactive Defense Strategies with Cyber Deception -- 6.5 Case Study -- 6.6 Conclusion -- References.
Part II Game Theory for Cyber Security -- Chapter 7 Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization -- 7.1 Introduction -- 7.1.1 Need for Cohesive Detection -- 7.1.2 Need for Strategic Detection -- 7.1.3 Minimax Detection (MAD) -- 7.2 Problem Formulation -- 7.2.1 System Model -- 7.2.2 Defense Model -- 7.2.3 Threat Model -- 7.2.4 Game Model -- 7.3 Main Result -- 7.3.1 Complexity Analysis -- 7.4 Illustrative Examples -- 7.5 Conclusion -- Acknowledgements -- References -- Chapter 8 Sensor Manipulation Games in Cyber Security -- 8.1 Introduction -- 8.2 Measurement Manipulation Games -- 8.2.1 Saddle‐Point Equilibria -- 8.2.2 Approximate Saddle‐Point Equilibrium -- 8.3 Sensor‐Reveal Games -- 8.3.1 Nash Equilibria -- 8.4 Conclusions and Future Work -- References -- Chapter 9 Adversarial Gaussian Process Regression in Sensor Networks -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Anomaly Detection with Gaussian Process Regression -- 9.4 Stealthy Attacks on Gaussian Process Anomaly Detection -- 9.5 The Resilient Anomaly Detection System -- 9.5.1 Resilient Anomaly Detection as a Stackelberg Game -- 9.5.2 Computing an Approximately Optimal Defense -- 9.6 Experiments -- 9.7 Conclusions -- References -- Chapter 10 Moving Target Defense Games for Cyber Security: Theory and Applications -- 10.1 Introduction -- 10.2 Moving Target Defense Theory -- 10.2.1 Game Theory for MTD -- 10.3 Single‐Controller Stochastic Games for Moving Target Defense -- 10.3.1 Stochastic Games -- 10.3.2 Single‐Controller Stochastic Games -- 10.3.2.1 Numerical Example -- 10.4 A Case Study for Applying Single‐Controller Stochastic Games in MTD The case study presented in this section is based on the work in Eldosouky et al. (). -- 10.4.1 Equilibrium Strategy Determination -- 10.4.2 Simulation Results and Analysis -- 10.5 Moving Target Defense Applications.
10.5.1 Internet of Things (IoT) Applications -- 10.5.2 Machine Learning Applications -- 10.5.3 Prospective MTD Applications -- 10.6 Conclusions -- References -- Chapter 11 Continuous Authentication Security Games -- 11.1 Introduction -- 11.2 Background and Related Work -- 11.3 Problem Formulation -- 11.3.1 User Behavior -- 11.3.2 Intrusion Detection System Model -- 11.3.3 Model of Continuous Authentication -- 11.3.4 System States without an Attacker -- 11.3.5 Attack Model -- 11.3.5.1 Listening (l(t)& -- equals -- r, a(t)& -- equals -- 0) -- 11.3.5.2 Attacking (l(t)& -- equals -- 0, a(t)& -- equals -- r) -- 11.3.5.3 Waiting (l(t)& -- equals -- 0, a(t)& -- equals -- 0) -- 11.3.6 Continuous Authentication Game -- 11.4 Optimal Attack Strategy under Asymmetric Information -- 11.4.1 MDP Formulation -- 11.4.1.1 Waiting (l(t)& -- equals -- 0, a(t)& -- equals -- 0) -- 11.4.1.2 Listening (l(t)& -- equals -- r, a(t)& -- equals -- 0) -- 11.4.1.3 Attacking (l(t)& -- equals -- 0, a(t)& -- equals -- r) -- 11.4.2 Optimality of the Threshold Policy -- 11.4.2.1 Optimality of Listening -- 11.4.2.2 Optimality of Attacking -- 11.5 Optimal Defense Strategy -- 11.5.1 Expected Defender Utility -- 11.5.2 Analysis without an Attacker -- 11.5.3 Analysis with an Attacker -- 11.6 Numerical Results -- 11.7 Conclusion and Discussion -- References -- Chapter 12 Cyber Autonomy in Software Security: Techniques and Tactics -- 12.1 Introduction -- 12.2 Background -- 12.3 Related Work -- 12.4 Model Setup -- 12.5 Techniques -- 12.6 Tactics -- 12.6.1 Model Parameters -- 12.6.2 Formalization -- 12.6.3 Finding Equilibriums -- 12.6.4 Algorithm -- 12.7 Case Study -- 12.8 Discussion -- 12.9 Conclusion -- References -- Part III Adversarial Machine Learning for Cyber Security.
Chapter 13 A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity Applications -- 13.1 Introduction to Game Theoretic Adversarial Machine Learning -- 13.2 Adversarial Learning Problem Definition -- 13.3 Game Theory in Adversarial Machine Learning -- 13.3.1 Simultaneous Games -- 13.3.1.1 Zero Sum Games -- 13.3.1.2 Nash Equilibrium Games -- 13.3.2 Sequential Games -- 13.4 Simultaneous Zero‐sum Games in Real Applications -- 13.4.1 Adversarial Attack Models -- 13.4.1.1 Free‐Range Attack -- 13.4.1.2 Restrained Attack -- 13.4.2 Adversarial SVM Learning -- 13.4.2.1 AD‐SVM Against Free‐range Attack Model -- 13.4.2.2 AD‐SVM Against Restrained Attack Model -- 13.4.3 Experiment -- 13.4.3.1 Attack Simulation -- 13.4.3.2 Experimental Results -- 13.4.3.3 A Few Words on Setting Cf, Cξ, and Cδ -- 13.4.4 Remark -- 13.5 Nested Bayesian Stackelberg Games -- 13.5.1 Adversarial Learning -- 13.5.2 A Single Leader Single Follower Stackelberg Game -- 13.5.3 Learning Models and Adversary Types -- 13.5.3.1 Learning Models -- 13.5.3.2 Adversary Types -- 13.5.3.3 Setting Payoff Matrices for the Single Leader Multiple‐followers Game -- 13.5.4 A Single Leader Multi‐followers Stackelberg Game -- 13.5.5 Experiments -- 13.5.5.1 Artificial Datasets -- 13.5.5.2 Real Datasets -- 13.5.6 Remark -- 13.6 Further Discussions -- Acknowledgements -- References -- Chapter 14 Adversarial Machine Learning for 5G Communications Security -- 14.1 Introduction -- 14.2 Adversarial Machine Learning -- 14.3 Adversarial Machine Learning in Wireless Communications -- 14.3.1 Wireless Attacks Built Upon Adversarial Machine Learning -- 14.3.2 Domain‐specific Challenges for Adversarial Machine Learning in Wireless Communications -- 14.3.3 Defense Schemes Against Adversarial Machine Learning -- 14.4 Adversarial Machine Learning in 5G Communications.
14.4.1 Scenario 1-Adversarial Attack on 5G Spectrum Sharing.
Record Nr. UNINA-9910555189803321
Hoboken, NJ : , : John Wiley & Sons, Inc., , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Game theory and machine learning for cyber security / / editors, Charles A. Kamhoua [et al.]
Game theory and machine learning for cyber security / / editors, Charles A. Kamhoua [et al.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc., , [2021]
Descrizione fisica 1 online resource (547 pages)
Disciplina 005.8
Soggetto topico Computer networks - Security measures
Game theory
Machine learning
ISBN 1-119-72394-9
1-119-72395-7
1-119-72391-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- Editor Biographies -- Contributors -- Foreword -- Preface -- Chapter 1 Introduction -- 1.1 Artificial Intelligence and Cybersecurity -- 1.1.1 Game Theory for Cybersecurity -- 1.1.2 Machine Learning for Cybersecurity -- 1.2 Overview -- References -- Part I Game Theory for Cyber Deception -- Chapter 2 Introduction to Game Theory -- 2.1 Overview -- 2.2 Example Two‐Player Zero‐Sum Games -- 2.3 Normal‐Form Games -- 2.3.1 Solution Concepts -- 2.4 Extensive‐Form Games -- 2.4.1 Solution Concepts -- 2.5 Stackelberg Game -- 2.5.1 Solution Concept -- 2.5.2 Stackelberg Security Games -- 2.5.3 Applications in Cybersecurity -- 2.6 Repeated Games -- 2.6.1 Solution Concepts -- 2.6.2 Applications in Cybersecurity -- 2.7 Bayesian Games -- 2.7.1 Solution Concepts -- 2.7.2 Applications in Cybersecurity -- 2.8 Stochastic Games -- 2.8.1 Solution Concepts -- 2.8.2 Applications in Cybersecurity -- References -- Chapter 3 Scalable Algorithms for Identifying Stealthy Attackers in a Game‐Theoretic Framework Using Deception -- 3.1 Introduction -- 3.2 Background -- 3.3 Case Studies -- 3.3.1 Case Study 1: Attackers with Same Exploits but Different Goals -- 3.3.2 Case Study 2: Attackers with Shared Exploits and Different Goals -- 3.3.3 Case Study 3: Attackers with Shared Exploits but Same Goals -- 3.4 Game Model -- 3.5 Defender Decision Making -- 3.6 Attacker Decision Making -- 3.7 Simulation Results -- 3.8 Scalability -- 3.8.1 Heuristics -- 3.9 Evaluation of Heuristics -- 3.10 Conclusions and Future Direction -- References -- Chapter 4 Honeypot Allocation Games over Attack Graphs for Cyber Deception -- 4.1 Introduction -- 4.2 System and Game Model -- 4.2.1 Attack Graph -- 4.2.2 General Game Formulation -- 4.2.2.1 Defender Action -- 4.2.2.2 Attacker Action -- 4.2.3 Reward Function -- 4.2.4 Mixed Strategy.
4.2.5 System Parameters -- 4.3 Allocating ℓ Honeypots Model -- 4.3.1 The Algorithm -- 4.4 Dynamic Honeypot Allocation -- 4.4.1 Mixed Strategy, State Evolution, and Objective Function -- 4.4.2 Q‐Minmax Algorithm -- 4.5 Numerical Results -- 4.6 Conclusion and Future Work -- Acknowledgment -- References -- Chapter 5 Evaluating Adaptive Deception Strategies for Cyber Defense with Human Adversaries -- 5.1 Introduction -- 5.1.1 HoneyGame: An Abstract Interactive Game to Study Deceptive Cyber Defense -- 5.2 An Ecology of Defense Algorithms -- 5.2.1 Static Pure Defender -- 5.2.2 Static Equilibrium Defender -- 5.2.3 Learning with Linear Rewards (LLR) -- 5.2.4 Best Response with Thompson sampling (BR‐TS) -- 5.2.5 Probabilistic Best Response with Thompson Sampling (PBR‐TS) -- 5.2.6 Follow the Regularized Leader (FTRL) -- 5.3 Experiments -- 5.3.1 Measures -- 5.4 Experiment 1 -- 5.4.1 Participants -- 5.4.2 Procedure -- 5.4.3 Results -- 5.4.3.1 Average Rewards -- 5.4.3.2 Attacks on Honeypots -- 5.4.3.3 Switching Behavior -- 5.4.3.4 Attack Distribution -- 5.5 Experiment 2 -- 5.5.1 Participants -- 5.5.2 Results -- 5.5.2.1 Average Rewards -- 5.5.2.2 Attacks on Honeypots -- 5.5.2.3 Switching Behavior -- 5.5.2.4 Attack Distribution -- 5.6 Towards Adaptive and Personalized Defense -- 5.7 Conclusions -- Acknowledgements -- References -- Chapter 6 A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception -- 6.1 Introduction -- 6.2 Attack‐Defend Games on Graph -- 6.2.1 Game Arena -- 6.2.2 Specifying the Security Properties in LTL -- 6.3 Hypergames on Graphs -- 6.4 Synthesis of Provably Secure Defense Strategies Using Hypergames on Graphs -- 6.4.1 Synthesis of Reactive Defense Strategies -- 6.4.2 Synthesis of Reactive Defense Strategies with Cyber Deception -- 6.5 Case Study -- 6.6 Conclusion -- References.
Part II Game Theory for Cyber Security -- Chapter 7 Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization -- 7.1 Introduction -- 7.1.1 Need for Cohesive Detection -- 7.1.2 Need for Strategic Detection -- 7.1.3 Minimax Detection (MAD) -- 7.2 Problem Formulation -- 7.2.1 System Model -- 7.2.2 Defense Model -- 7.2.3 Threat Model -- 7.2.4 Game Model -- 7.3 Main Result -- 7.3.1 Complexity Analysis -- 7.4 Illustrative Examples -- 7.5 Conclusion -- Acknowledgements -- References -- Chapter 8 Sensor Manipulation Games in Cyber Security -- 8.1 Introduction -- 8.2 Measurement Manipulation Games -- 8.2.1 Saddle‐Point Equilibria -- 8.2.2 Approximate Saddle‐Point Equilibrium -- 8.3 Sensor‐Reveal Games -- 8.3.1 Nash Equilibria -- 8.4 Conclusions and Future Work -- References -- Chapter 9 Adversarial Gaussian Process Regression in Sensor Networks -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Anomaly Detection with Gaussian Process Regression -- 9.4 Stealthy Attacks on Gaussian Process Anomaly Detection -- 9.5 The Resilient Anomaly Detection System -- 9.5.1 Resilient Anomaly Detection as a Stackelberg Game -- 9.5.2 Computing an Approximately Optimal Defense -- 9.6 Experiments -- 9.7 Conclusions -- References -- Chapter 10 Moving Target Defense Games for Cyber Security: Theory and Applications -- 10.1 Introduction -- 10.2 Moving Target Defense Theory -- 10.2.1 Game Theory for MTD -- 10.3 Single‐Controller Stochastic Games for Moving Target Defense -- 10.3.1 Stochastic Games -- 10.3.2 Single‐Controller Stochastic Games -- 10.3.2.1 Numerical Example -- 10.4 A Case Study for Applying Single‐Controller Stochastic Games in MTD The case study presented in this section is based on the work in Eldosouky et al. (). -- 10.4.1 Equilibrium Strategy Determination -- 10.4.2 Simulation Results and Analysis -- 10.5 Moving Target Defense Applications.
10.5.1 Internet of Things (IoT) Applications -- 10.5.2 Machine Learning Applications -- 10.5.3 Prospective MTD Applications -- 10.6 Conclusions -- References -- Chapter 11 Continuous Authentication Security Games -- 11.1 Introduction -- 11.2 Background and Related Work -- 11.3 Problem Formulation -- 11.3.1 User Behavior -- 11.3.2 Intrusion Detection System Model -- 11.3.3 Model of Continuous Authentication -- 11.3.4 System States without an Attacker -- 11.3.5 Attack Model -- 11.3.5.1 Listening (l(t)& -- equals -- r, a(t)& -- equals -- 0) -- 11.3.5.2 Attacking (l(t)& -- equals -- 0, a(t)& -- equals -- r) -- 11.3.5.3 Waiting (l(t)& -- equals -- 0, a(t)& -- equals -- 0) -- 11.3.6 Continuous Authentication Game -- 11.4 Optimal Attack Strategy under Asymmetric Information -- 11.4.1 MDP Formulation -- 11.4.1.1 Waiting (l(t)& -- equals -- 0, a(t)& -- equals -- 0) -- 11.4.1.2 Listening (l(t)& -- equals -- r, a(t)& -- equals -- 0) -- 11.4.1.3 Attacking (l(t)& -- equals -- 0, a(t)& -- equals -- r) -- 11.4.2 Optimality of the Threshold Policy -- 11.4.2.1 Optimality of Listening -- 11.4.2.2 Optimality of Attacking -- 11.5 Optimal Defense Strategy -- 11.5.1 Expected Defender Utility -- 11.5.2 Analysis without an Attacker -- 11.5.3 Analysis with an Attacker -- 11.6 Numerical Results -- 11.7 Conclusion and Discussion -- References -- Chapter 12 Cyber Autonomy in Software Security: Techniques and Tactics -- 12.1 Introduction -- 12.2 Background -- 12.3 Related Work -- 12.4 Model Setup -- 12.5 Techniques -- 12.6 Tactics -- 12.6.1 Model Parameters -- 12.6.2 Formalization -- 12.6.3 Finding Equilibriums -- 12.6.4 Algorithm -- 12.7 Case Study -- 12.8 Discussion -- 12.9 Conclusion -- References -- Part III Adversarial Machine Learning for Cyber Security.
Chapter 13 A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity Applications -- 13.1 Introduction to Game Theoretic Adversarial Machine Learning -- 13.2 Adversarial Learning Problem Definition -- 13.3 Game Theory in Adversarial Machine Learning -- 13.3.1 Simultaneous Games -- 13.3.1.1 Zero Sum Games -- 13.3.1.2 Nash Equilibrium Games -- 13.3.2 Sequential Games -- 13.4 Simultaneous Zero‐sum Games in Real Applications -- 13.4.1 Adversarial Attack Models -- 13.4.1.1 Free‐Range Attack -- 13.4.1.2 Restrained Attack -- 13.4.2 Adversarial SVM Learning -- 13.4.2.1 AD‐SVM Against Free‐range Attack Model -- 13.4.2.2 AD‐SVM Against Restrained Attack Model -- 13.4.3 Experiment -- 13.4.3.1 Attack Simulation -- 13.4.3.2 Experimental Results -- 13.4.3.3 A Few Words on Setting Cf, Cξ, and Cδ -- 13.4.4 Remark -- 13.5 Nested Bayesian Stackelberg Games -- 13.5.1 Adversarial Learning -- 13.5.2 A Single Leader Single Follower Stackelberg Game -- 13.5.3 Learning Models and Adversary Types -- 13.5.3.1 Learning Models -- 13.5.3.2 Adversary Types -- 13.5.3.3 Setting Payoff Matrices for the Single Leader Multiple‐followers Game -- 13.5.4 A Single Leader Multi‐followers Stackelberg Game -- 13.5.5 Experiments -- 13.5.5.1 Artificial Datasets -- 13.5.5.2 Real Datasets -- 13.5.6 Remark -- 13.6 Further Discussions -- Acknowledgements -- References -- Chapter 14 Adversarial Machine Learning for 5G Communications Security -- 14.1 Introduction -- 14.2 Adversarial Machine Learning -- 14.3 Adversarial Machine Learning in Wireless Communications -- 14.3.1 Wireless Attacks Built Upon Adversarial Machine Learning -- 14.3.2 Domain‐specific Challenges for Adversarial Machine Learning in Wireless Communications -- 14.3.3 Defense Schemes Against Adversarial Machine Learning -- 14.4 Adversarial Machine Learning in 5G Communications.
14.4.1 Scenario 1-Adversarial Attack on 5G Spectrum Sharing.
Record Nr. UNINA-9910830628803321
Hoboken, NJ : , : John Wiley & Sons, Inc., , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Modeling and design of secure Internet of things / / edited by Charles A. Kamhoua, Laurent L. Njilla, Alexander Kott, Sachin Shetty
Modeling and design of secure Internet of things / / edited by Charles A. Kamhoua, Laurent L. Njilla, Alexander Kott, Sachin Shetty
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley-IEEE Press, , [2020]
Descrizione fisica 1 online resource (697 pages)
Disciplina 005.8
Collana IEEE press
Soggetto topico Internet of things - Security measures
ISBN 1-119-59339-5
1-119-59337-9
1-119-59338-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto About the Editors ix -- List of Contributors xiii -- Foreword xix -- Preface xxiii -- 1 Introduction 1 /Charles A. Kamhoua, Laurent L. Njilla, Alexander Kott, and Sachin Shetty -- Part I Game Theory for Cyber Deception 27 -- 2 Game-Theoretic Analysis of Cyber Deception: Evidence-Based Strategies and Dynamic Risk Mitigation 29 /Tao Zhang, Linan Huang, Jeffrey Pawlick, and Quanyan Zhu -- 3 A Hypergame-Based Defense Strategy Toward Cyber Deception in Internet of Battlefield Things (IoBT) 59 /Bowei Xi and Charles A. Kamhoua -- 4 Cooperative Spectrum Sharing and Trust Management in IoT Networks 79 /Fatemeh Afghah, Alireza Shamsoshoara, Laurent L. Njilla, and Charles A. Kamhoua -- 5 Adaptation and Deception in Adversarial Cyber Operations 111 /George Cybenko -- 6 On Development of a Game-Theoretic Model for Deception-Based Security 123 /Satyaki Nan, Swastik Brahma, Charles A. Kamhoua, and Laurent L. Njilla -- 7 Deception for Cyber Adversaries: Status, Challenges, and Perspectives 141 /Abdullah Alshammari, Danda B. Rawat, Moses Garuba, Charles A. Kamhoua, and Laurent L. Njilla -- Part II IoT Security Modeling and Analysis 161 -- 8 Cyber-Physical Vulnerability Analysis of IoT Applications Using Multi-Modeling 163 /Ted Bapty, Abhishek Dubey, and Janos Sztipanovits -- 9 Securing Smart Cities: Implications and Challenges 185 /Ioannis Agadakos, Prashant Anantharaman, Gabriela F. Ciocarlie, Bogdan Copos, Michael Emmi, Tancrd̈e Lepoint, Ulf Lindqvist, Michael Locasto, and Liwei Song -- 10 Modeling and Analysis of Integrated Proactive Defense Mechanisms for Internet of Things 217 /Mengmeng Ge, Jin-Hee Cho, Bilal Ishfaq, and Dong Seong Kim -- 11 Addressing Polymorphic Advanced Threats in Internet of Things Networks by Cross-Layer Profiling 249 /Hisham Alasmary, Afsah Anwar, Laurent L. Njilla, Charles A. Kamhoua, and Aziz Mohaisen -- 12 Analysis of Stepping-Stone Attacks in Internet of Things Using Dynamic Vulnerability Graphs 273 /Marco Gamarra, Sachin Shetty, Oscar Gonzalez, David M. Nicol, Charles A. Kamhoua, and Laurent L. Njilla.
13 Anomaly Behavior Analysis of IoT Protocols 295 /Pratik Satam, Shalaka Satam, Salim Hariri, and Amany Alshawi -- 14 Dynamic Cyber Deception Using Partially Observable Monte-Carlo Planning Framework 331 /Md Ali Reza Al Amin, Sachin Shetty, Laurent L. Njilla, Deepak K. Tosh, and Charles A. Kamhoua -- 15 A Coding Theoretic View of Secure State Reconstruction 357 /Suhas Diggavi and Paulo Tabuada -- 16 Governance for the Internet of Things: Striving Toward Resilience 371 /S. E. Galaitsi, Benjamin D. Trump, and Igor Linkov -- Part III IoT Security Design 383 -- 17 Secure and Resilient Control of IoT-Based 3D Printers 385 /Zhiheng Xu and Quanyan Zhu -- 18 Proactive Defense Against Security Threats on IoT Hardware 407 /Qiaoyan Yu, Zhiming Zhang, and Jaya Dofe -- 19 IoT Device Attestation: From a Cross-Layer Perspective 435 /Orlando Arias, Fahim Rahman, Mark Tehranipoor, and Yier Jin -- 20 Software-Defined Networking for Cyber Resilience in Industrial Internet of Things (IIoT) 453 /Kamrul Hasan, Sachin Shetty, Amin Hassanzadeh, Malek Ben Salem, and Jay Chen -- 21 Leverage SDN for Cyber-Security Deception in Internet of Things 479 /Yaoqing Liu, Garegin Grigoryan, Charles A. Kamhoua, and Laurent L. Njilla -- 22 Decentralized Access Control for IoT Based on Blockchain and Smart Contract 505 /Ronghua Xu, Yu Chen, and Erik Blasch -- 23 Intent as a Secure Design Primitive 529 /Prashant Anantharaman, J. Peter Brady, Ira Ray Jenkins, Vijay H. Kothari, Michael C. Millian, Kartik Palani, Kirti V. Rathore, Jason Reeves, Rebecca Shapiro, Syed H. Tanveer, Sergey Bratus, and Sean W. Smith -- 24 A Review of Moving Target Defense Mechanisms for Internet of Things Applications 563 /Nico Saputro, Samet Tonyali, Abdullah Aydeger, Kemal Akkaya, Mohammad A. Rahman, and Selcuk Uluagac -- 25 Toward Robust Outlier Detector for Internet of Things Applications 615 /Raj Mani Shukla and Shamik Sengupta -- 26 Summary and Future Work 635 /Charles A. Kamhoua, Laurent L. Njilla, Alexander Kott, and Sachin Shetty.
Index 647.
Record Nr. UNINA-9910555075203321
Hoboken, New Jersey : , : Wiley-IEEE Press, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Modeling and design of secure Internet of things / / edited by Charles A. Kamhoua, Laurent L. Njilla, Alexander Kott, Sachin Shetty
Modeling and design of secure Internet of things / / edited by Charles A. Kamhoua, Laurent L. Njilla, Alexander Kott, Sachin Shetty
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley-IEEE Press, , [2020]
Descrizione fisica 1 online resource (697 pages)
Disciplina 005.8
Collana IEEE press
Soggetto topico Internet of things - Security measures
ISBN 1-119-59339-5
1-119-59337-9
1-119-59338-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto About the Editors ix -- List of Contributors xiii -- Foreword xix -- Preface xxiii -- 1 Introduction 1 /Charles A. Kamhoua, Laurent L. Njilla, Alexander Kott, and Sachin Shetty -- Part I Game Theory for Cyber Deception 27 -- 2 Game-Theoretic Analysis of Cyber Deception: Evidence-Based Strategies and Dynamic Risk Mitigation 29 /Tao Zhang, Linan Huang, Jeffrey Pawlick, and Quanyan Zhu -- 3 A Hypergame-Based Defense Strategy Toward Cyber Deception in Internet of Battlefield Things (IoBT) 59 /Bowei Xi and Charles A. Kamhoua -- 4 Cooperative Spectrum Sharing and Trust Management in IoT Networks 79 /Fatemeh Afghah, Alireza Shamsoshoara, Laurent L. Njilla, and Charles A. Kamhoua -- 5 Adaptation and Deception in Adversarial Cyber Operations 111 /George Cybenko -- 6 On Development of a Game-Theoretic Model for Deception-Based Security 123 /Satyaki Nan, Swastik Brahma, Charles A. Kamhoua, and Laurent L. Njilla -- 7 Deception for Cyber Adversaries: Status, Challenges, and Perspectives 141 /Abdullah Alshammari, Danda B. Rawat, Moses Garuba, Charles A. Kamhoua, and Laurent L. Njilla -- Part II IoT Security Modeling and Analysis 161 -- 8 Cyber-Physical Vulnerability Analysis of IoT Applications Using Multi-Modeling 163 /Ted Bapty, Abhishek Dubey, and Janos Sztipanovits -- 9 Securing Smart Cities: Implications and Challenges 185 /Ioannis Agadakos, Prashant Anantharaman, Gabriela F. Ciocarlie, Bogdan Copos, Michael Emmi, Tancrd̈e Lepoint, Ulf Lindqvist, Michael Locasto, and Liwei Song -- 10 Modeling and Analysis of Integrated Proactive Defense Mechanisms for Internet of Things 217 /Mengmeng Ge, Jin-Hee Cho, Bilal Ishfaq, and Dong Seong Kim -- 11 Addressing Polymorphic Advanced Threats in Internet of Things Networks by Cross-Layer Profiling 249 /Hisham Alasmary, Afsah Anwar, Laurent L. Njilla, Charles A. Kamhoua, and Aziz Mohaisen -- 12 Analysis of Stepping-Stone Attacks in Internet of Things Using Dynamic Vulnerability Graphs 273 /Marco Gamarra, Sachin Shetty, Oscar Gonzalez, David M. Nicol, Charles A. Kamhoua, and Laurent L. Njilla.
13 Anomaly Behavior Analysis of IoT Protocols 295 /Pratik Satam, Shalaka Satam, Salim Hariri, and Amany Alshawi -- 14 Dynamic Cyber Deception Using Partially Observable Monte-Carlo Planning Framework 331 /Md Ali Reza Al Amin, Sachin Shetty, Laurent L. Njilla, Deepak K. Tosh, and Charles A. Kamhoua -- 15 A Coding Theoretic View of Secure State Reconstruction 357 /Suhas Diggavi and Paulo Tabuada -- 16 Governance for the Internet of Things: Striving Toward Resilience 371 /S. E. Galaitsi, Benjamin D. Trump, and Igor Linkov -- Part III IoT Security Design 383 -- 17 Secure and Resilient Control of IoT-Based 3D Printers 385 /Zhiheng Xu and Quanyan Zhu -- 18 Proactive Defense Against Security Threats on IoT Hardware 407 /Qiaoyan Yu, Zhiming Zhang, and Jaya Dofe -- 19 IoT Device Attestation: From a Cross-Layer Perspective 435 /Orlando Arias, Fahim Rahman, Mark Tehranipoor, and Yier Jin -- 20 Software-Defined Networking for Cyber Resilience in Industrial Internet of Things (IIoT) 453 /Kamrul Hasan, Sachin Shetty, Amin Hassanzadeh, Malek Ben Salem, and Jay Chen -- 21 Leverage SDN for Cyber-Security Deception in Internet of Things 479 /Yaoqing Liu, Garegin Grigoryan, Charles A. Kamhoua, and Laurent L. Njilla -- 22 Decentralized Access Control for IoT Based on Blockchain and Smart Contract 505 /Ronghua Xu, Yu Chen, and Erik Blasch -- 23 Intent as a Secure Design Primitive 529 /Prashant Anantharaman, J. Peter Brady, Ira Ray Jenkins, Vijay H. Kothari, Michael C. Millian, Kartik Palani, Kirti V. Rathore, Jason Reeves, Rebecca Shapiro, Syed H. Tanveer, Sergey Bratus, and Sean W. Smith -- 24 A Review of Moving Target Defense Mechanisms for Internet of Things Applications 563 /Nico Saputro, Samet Tonyali, Abdullah Aydeger, Kemal Akkaya, Mohammad A. Rahman, and Selcuk Uluagac -- 25 Toward Robust Outlier Detector for Internet of Things Applications 615 /Raj Mani Shukla and Shamik Sengupta -- 26 Summary and Future Work 635 /Charles A. Kamhoua, Laurent L. Njilla, Alexander Kott, and Sachin Shetty.
Index 647.
Record Nr. UNINA-9910830652403321
Hoboken, New Jersey : , : Wiley-IEEE Press, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui