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5G and Beyond Wireless Communication Networks
| 5G and Beyond Wireless Communication Networks |
| Autore | Sun Haijian |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2023 |
| Descrizione fisica | 1 online resource (211 pages) |
| Altri autori (Persone) |
HuRose Qingyang
QianYi |
| Collana | IEEE Press Series |
| ISBN |
1-119-08949-2
1-119-08946-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- Chapter 1 Introduction to 5G and Beyond Network -- 1.1 5G and Beyond System Requirements -- 1.1.1 Technical Challenges -- 1.2 Enabling Technologies -- 1.2.1 5G New Radio -- 1.2.1.1 Non‐orthogonal Multiple Access (NOMA) -- 1.2.1.2 Channel Codes -- 1.2.1.3 Massive MIMO -- 1.2.1.4 Other 5G NR Techniques -- 1.2.2 Mobile Edge Computing (MEC) -- 1.2.3 Hybrid and Heterogeneous Communication Architecture for Pervasive IoTs -- 1.3 Book Outline -- Chapter 2 5G Wireless Networks with Underlaid D2D Communications -- 2.1 Background -- 2.1.1 MU‐MIMO -- 2.1.2 D2D Communication -- 2.1.3 MU‐MIMO and D2D in 5G -- 2.2 NOMA‐Aided Network with Underlaid D2D -- 2.3 NOMA with SIC and Problem Formation -- 2.3.1 NOMA with SIC -- 2.3.2 Problem Formation -- 2.4 Precoding and User Grouping Algorithm -- 2.4.1 Zero‐Forcing Beamforming -- 2.4.1.1 First ZF Precoding -- 2.4.1.2 Second ZF Precoding -- 2.4.2 User Grouping and Optimal Power Allocation -- 2.4.2.1 First ZF Precoding -- 2.4.2.2 Second ZF Precoding -- 2.5 Numerical Results -- 2.6 Summary -- Chapter 3 5G NOMA‐Enabled Wireless Networks -- 3.1 Background -- 3.2 Error Propagation in NOMA -- 3.3 SIC and Problem Formulation -- 3.3.1 SIC with Error Propagation -- 3.3.2 Problem Formation -- 3.4 Precoding and Power Allocation -- 3.4.1 Precoding Design -- 3.4.2 Case Studies for Power Allocation -- 3.4.2.1 Case I -- 3.4.2.2 Case II -- 3.5 Numerical Results -- 3.6 Summary -- Chapter 4 NOMA in Relay and IoT for 5G Wireless Networks -- 4.1 Outage Probability Study in a NOMA Relay System -- 4.1.1 Background -- 4.1.2 System Model -- 4.1.2.1 NOMA Cooperative Scheme -- 4.1.2.2 NOMA TDMA Scheme -- 4.1.3 Outage Probability Analysis -- 4.1.3.1 Outage Probability in NOMA Cooperative Scheme -- 4.1.4 Outage Probability in NOMA TDMA Scheme.
4.1.5 Outage Probability with Error Propagation in SIC -- 4.1.5.1 Outage Probability in NOMA Cooperative Scheme with EP -- 4.1.5.2 Outage Probability in NOMA TDMA Scheme with EP -- 4.1.6 Numerical Results -- 4.2 NOMA in a mmWave‐Based IoT Wireless System with SWIPT -- 4.2.1 Introduction -- 4.2.2 System Model -- 4.2.2.1 Phase 1 Transmission -- 4.2.2.2 Phase 2 Transmission -- 4.2.3 Outage Analysis -- 4.2.3.1 UE 1 Outage Probability -- 4.2.3.2 UE 2 Outage Probability -- 4.2.3.3 Outage at High SNR -- 4.2.3.4 Diversity Analysis for UE 2 -- 4.2.4 Numerical Results -- 4.2.5 Summary -- Chapter 5 Robust Beamforming in NOMA Cognitive Radio Networks: Bounded CSI -- 5.1 Background -- 5.1.1 Related Work and Motivation -- 5.1.1.1 Linear EH Model -- 5.1.1.2 Non‐linear EH Model -- 5.1.2 Contributions -- 5.2 System and Energy Harvesting Models -- 5.2.1 System Model -- 5.2.2 Non‐linear EH Model -- 5.2.3 Bounded CSI Error Model -- 5.2.3.1 NOMA Transmission -- 5.3 Power Minimization‐Based Problem Formulation -- 5.3.1 Problem Formulation -- 5.3.2 Matrix Decomposition -- 5.4 Maximum Harvested Energy Problem Formulation -- 5.4.1 Complexity Analysis -- 5.5 Numerical Results -- 5.5.1 Power Minimization Problem -- 5.5.2 Energy Harvesting Maximization Problem -- 5.6 Summary -- Chapter 6 Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI -- 6.1 Gaussian CSI Error Model -- 6.2 Power Minimization‐Based Problem Formulation -- 6.2.1 Bernstein‐Type Inequality I -- 6.2.2 Bernstein‐Type Inequality II -- 6.3 Maximum Harvested Energy Problem Formulation -- 6.3.1 Complexity Analysis -- 6.4 Numerical Results -- 6.4.1 Power Minimization Problem -- 6.4.2 Energy Harvesting Maximization Problem -- 6.5 Summary -- Chapter 7 Mobile Edge Computing in 5G Wireless Networks -- 7.1 Background -- 7.2 System Model -- 7.2.1 Data Offloading -- 7.2.2 Local Computing. 7.3 Problem Formulation -- 7.3.1 Update pk, tk, and fk -- 7.3.2 Update Lagrange Multipliers -- 7.3.3 Update Auxiliary Variables -- 7.3.4 Complexity Analysis -- 7.4 Numerical Results -- 7.5 Summary -- Chapter 8 Toward Green MEC Offloading with Security Enhancement -- 8.1 Background -- 8.2 System Model -- 8.2.1 Secure Offloading -- 8.2.2 Local Computing -- 8.2.3 Receiving Computed Results -- 8.2.4 Computation Efficiency in MEC Systems -- 8.3 Computation Efficiency Maximization with Active Eavesdropper -- 8.3.1 SCA‐Based Optimization Algorithm -- 8.3.2 Objective Function -- 8.3.3 Proposed Solution to P4 with given (λk,βk) -- 8.3.4 Update (λk,βk) -- 8.4 Numerical Results -- 8.5 Summary -- Chapter 9 Wireless Systems for Distributed Machine Learning -- 9.1 Background -- 9.2 System Model -- 9.2.1 FL Model Update -- 9.2.2 Gradient Quantization -- 9.2.3 Gradient Sparsification -- 9.3 FL Model Update with Adaptive NOMA Transmission -- 9.3.1 Uplink NOMA Transmission -- 9.3.2 NOMA Scheduling -- 9.3.3 Adaptive Transmission -- 9.4 Scheduling and Power Optimization -- 9.4.1 Problem Formulation -- 9.5 Scheduling Algorithm and Power Allocation -- 9.5.1 Scheduling Graph Construction -- 9.5.2 Optimal scheduling Pattern -- 9.5.3 Power Allocation -- 9.6 Numerical Results -- 9.7 Summary -- Chapter 10 Secure Spectrum Sharing with Machine Learning: An Overview -- 10.1 Background -- 10.1.1 SS: A Brief History -- 10.1.2 Security Issues in SS -- 10.2 ML‐Based Methodologies for SS -- 10.2.1 ML‐Based CRN -- 10.2.1.1 Spectrum Sensing -- 10.2.1.2 Spectrum Selection -- 10.2.1.3 Spectrum Access -- 10.2.1.4 Spectrum Handoff -- 10.2.2 Database‐Assisted SS -- 10.2.2.1 ML‐Based EZ Optimization -- 10.2.2.2 Incumbent Detection -- 10.2.2.3 Channel Selection and Transaction -- 10.2.3 ML‐Based LTE‐U/LTE‐LAA -- 10.2.3.1 ML‐Based LBT Methods -- 10.2.3.2 ML‐Based Duty Cycle Methods. 10.2.3.3 Game‐Theory‐Based Methods -- 10.2.3.4 Distributed‐Algorithm‐Based Methods -- 10.2.4 Ambient Backscatter Networks -- 10.2.4.1 Information Extraction -- 10.2.4.2 Operating Mode Selection and User Coordination -- 10.2.4.3 AmBC‐CR Methods -- 10.3 Summary -- Chapter 11 Secure Spectrum Sharing with Machine Learning: Methodologies -- 11.1 Security Concerns in SS -- 11.1.1 Primary User Emulation Attack -- 11.1.2 Spectrum Sensing Data Falsification Attack -- 11.1.3 Jamming Attacks -- 11.1.4 Intercept/Eavesdrop -- 11.1.5 Privacy Issues in Database‐Assisted SS Systems -- 11.2 ML‐Assisted Secure SS -- 11.2.1 State‐of‐the‐Art Methods of Defense Against PUE Attack -- 11.2.1.1 ML‐Based Detection Methods -- 11.2.1.2 Robust Detection Methods -- 11.2.1.3 ML‐Based Attack Methods -- 11.2.2 State‐of‐the‐Art Methods of Defense Against SSDF Attack -- 11.2.2.1 Outlier Detection Methods -- 11.2.2.2 Reputation‐Based Detection Methods -- 11.2.2.3 SSDF and PUE Combination Attacks -- 11.2.3 State‐of‐the‐Art Methods of Defense Against Jamming Attacks -- 11.2.3.1 ML‐Based Anti‐Jamming Methods -- 11.2.3.2 Attacker Enhanced Anti‐Jamming Methods -- 11.2.3.3 AmBC Empowered Anti‐Jamming Methods -- 11.2.4 State‐of‐the‐Art Methods of Defense Against Intercept/Eavesdrop -- 11.2.4.1 RL‐Based Anti‐Eavesdropping Methods -- 11.2.5 State‐of‐the‐Art ML‐Based Privacy Protection Methods -- 11.2.5.1 Privacy Protection for PUs in SS Networks -- 11.2.5.2 Privacy Protection for SUs in SS Networks -- 11.2.5.3 Privacy Protection for ML Algorithms -- 11.3 Summary -- Chapter 12 Open Issues and Future Directions for 5G and Beyond Wireless Networks -- 12.1 Joint Communication and Sensing -- 12.2 Space‐Air‐Ground Communication -- 12.3 Semantic Communication -- 12.4 Data‐Driven Communication System Design -- Appendix A Proof of Theorem 5.1 -- Bibliography -- Index -- EULA. |
| Record Nr. | UNINA-9910830064303321 |
Sun Haijian
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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5G Wireless Network Security and Privacy / / DongFeng Fang, Yi Qian, and Rose Qingyang Hu
| 5G Wireless Network Security and Privacy / / DongFeng Fang, Yi Qian, and Rose Qingyang Hu |
| Autore | Fang Dongfeng |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Chichester, England : , : John Wiley & Sons Ltd, , [2024] |
| Descrizione fisica | 1 online resource (131 pages) |
| Disciplina | 621.3845/6 |
| Collana | IEEE Press Series |
| Soggetto topico | 5G mobile communication systems - Security measures |
| ISBN |
1-119-78431-X
1-119-78434-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- Introduction -- Chapter 1 Introduction to 5G Wireless Systems -- 1.1 Motivations and Objectives of 5G Wireless Networks -- 1.2 Security Drives and Requirements -- 1.3 5G Wireless Network Architecture -- 1.3.1 Overview of the 5G Wireless Network Architecture -- 1.3.2 Comparison Between the Legacy Cellular Network and the 5G Wireless Network -- 1.4 Conclusion -- Chapter 2 Security from Legacy Wireless Systems to 5G Networks -- 2.1 Network Security for Legacy Systems -- 2.2 Security Attacks and Security Services in 5G Wireless Networks -- 2.2.1 Security Attacks -- 2.2.2 Security Services -- 2.2.2.1 Authentication -- 2.2.2.2 Confidentiality -- 2.2.2.3 Availability -- 2.2.2.4 Integrity -- 2.3 The Evolution of Wireless Security Architectures from 3G to 5G -- 2.3.1 3G Security Architecture -- 2.3.2 4G Security Architecture -- 2.3.3 5G Wireless Security Architecture -- 2.3.3.1 Overview of the Proposed 5G Wireless Security Architecture -- 2.3.3.2 Security Domains -- 2.4 Summary -- Chapter 3 Security Mechanisms in 5G Wireless Systems -- 3.1 Cryptographic Approaches and Physical Layer Security -- 3.2 Authentication -- 3.3 Availability -- 3.4 Data Confidentiality -- 3.5 Key Management -- 3.6 Privacy -- 3.7 Conclusion -- Chapter 4 An Efficient Security Solution Based on Physical Layer Security in 5G Wireless Networks -- 4.1 Enhancing 5G Security Through Artificial Noise and Interference Utilization -- 4.2 A HetNet System Model and Security Analysis -- 4.2.1 System Model and Threat Model -- 4.2.2 Security Analysis -- 4.3 Problem Formulation and Analysis -- 4.3.1 Maximum Secrecy Rate -- 4.3.2 The Proposed Algorithm -- 4.4 Numerical and Simulation Results -- 4.5 Conclusion.
Chapter 5 Flexible and Efficient Security Schemes for IoT Applications in 5G Wireless Systems -- 5.1 IoT Application Models and Current Security Challenges -- 5.2 A General System Model for IoT Applications Over 5G -- 5.2.1 System Architecture -- 5.2.2 Trust Models -- 5.2.3 Threat Models and Design Objectives -- 5.3 The 5G Authentication and Secure Data Transmission Scheme -- 5.3.1 Overview of the 5G Authentication and Secure Data Transmission Scheme -- 5.3.2 The Detailed Scheme -- 5.3.2.1 Phase 1 - System Initialization -- 5.3.2.2 Phase 2 - Authentication and Initial Session Key Agreement -- 5.3.2.3 Phase 3 - Data Transmission -- 5.3.2.4 Phase 4 - Data Receiving -- 5.3.2.5 Phase 5 - T2 IoT Devices Authentication and Initial Session Key Agreement -- 5.4 Security Analysis -- 5.4.1 Protocol Verification -- 5.4.2 Security Objectives -- 5.4.2.1 Mutual Authentication -- 5.4.2.2 Initial Session Key Agreement -- 5.4.2.3 Data Confidentiality and Integrity -- 5.4.2.4 Contextual Privacy -- 5.4.2.5 Forward Security -- 5.4.2.6 End‐to‐End Security -- 5.4.2.7 Key Escrow Resilience -- 5.5 Performance Evaluation -- 5.5.1 Security Services -- 5.5.2 Computational Overhead -- 5.5.3 Communication Overhead -- 5.6 Conclusion -- Chapter 6 Secure and Efficient Mobility Management in 5G Wireless Networks -- 6.1 Handover Issues and Requirements Over 5G Wireless Networks -- 6.2 A 5G CN Model and HetNet System Model -- 6.3 5G Handover Scenarios and Procedures -- 6.3.1 Handover Scenarios -- 6.3.2 Handover Procedures -- 6.4 A New Authentication Protocol for 5G Networks -- 6.4.1 Assumptions -- 6.4.2 Pre‐Authentication -- 6.4.3 Full Authentication -- 6.4.4 Fast Authentication -- 6.4.4.1 Handover Between APs -- 6.4.4.2 Handover Between BSs -- 6.5 Security Analysis of the New 5G Authentication Protocols -- 6.6 Performance Evaluations -- 6.6.1 Communication Overhead. 6.6.2 Computation Overhead -- 6.7 Conclusion -- Chapter 7 Open Issues and Future Research Directions for Security and Privacy in 5G Networks -- 7.1 New Trust Models -- 7.2 New Security Attack Models -- 7.3 Privacy Protection -- 7.4 Unified Security Management -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910830427503321 |
|
Fang Dongfeng
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| Chichester, England : , : John Wiley & Sons Ltd, , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Cybersecurity in Intelligent Networking Systems
| Cybersecurity in Intelligent Networking Systems |
| Autore | Xu Shengjie |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (147 pages) |
| Altri autori (Persone) |
QianYi
HuRose Qingyang |
| Collana | IEEE Press Ser. |
| ISBN |
1-119-78413-1
1-119-78410-7 1-119-78412-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Cybersecurity in the Era of Artificial Intelligence -- 1.1 Artificial Intelligence for Cybersecurity -- 1.1.1 Artificial Intelligence -- 1.1.2 Machine Learning -- 1.1.2.1 Supervised Learning -- 1.1.2.2 Unsupervised Learning -- 1.1.2.3 Semi‐supervised Learning -- 1.1.2.4 Reinforcement Learning -- 1.1.3 Data‐Driven Workflow for Cybersecurity -- 1.2 Key Areas and Challenges -- 1.2.1 Anomaly Detection -- 1.2.2 Trustworthy Artificial Intelligence -- 1.2.3 Privacy Preservation -- 1.3 Toolbox to Build Secure and Intelligent Systems -- 1.3.1 Machine Learning and Deep Learning -- 1.3.1.1 NumPy -- 1.3.1.2 SciPy -- 1.3.1.3 Scikit‐learn -- 1.3.1.4 PyTorch -- 1.3.1.5 TensorFlow -- 1.3.2 Privacy‐Preserving Machine Learning -- 1.3.2.1 Syft -- 1.3.2.2 TensorFlow Federated -- 1.3.2.3 TensorFlow Privacy -- 1.3.3 Adversarial Machine Learning -- 1.3.3.1 SecML and SecML Malware -- 1.3.3.2 Foolbox -- 1.3.3.3 CleverHans -- 1.3.3.4 Counterfit -- 1.3.3.5 MintNV -- 1.4 Data Repositories for Cybersecurity Research -- 1.4.1 NSL‐KDD -- 1.4.2 UNSW‐NB15 -- 1.4.3 EMBER -- 1.5 Summary -- Notes -- References -- Chapter 2 Cyber Threats and Gateway Defense -- 2.1 Cyber Threats -- 2.1.1 Cyber Intrusions -- 2.1.2 Distributed Denial of Services Attack -- 2.1.3 Malware and Shellcode -- 2.2 Gateway Defense Approaches -- 2.2.1 Network Access Control -- 2.2.2 Anomaly Isolation -- 2.2.3 Collaborative Learning -- 2.2.4 Secure Local Data Learning -- 2.3 Emerging Data‐driven Methods for Gateway Defense -- 2.3.1 Semi‐supervised Learning for Intrusion Detection -- 2.3.2 Transfer Learning for Intrusion Detection -- 2.3.3 Federated Learning for Privacy Preservation -- 2.3.4 Reinforcement Learning for Penetration Test.
2.4 Case Study: Reinforcement Learning for Automated Post‐breach Penetration Test -- 2.4.1 Literature Review -- 2.4.2 Research Idea -- 2.4.3 Training Agent Using Deep Q‐Learning -- 2.5 Summary -- References -- Chapter 3 Edge Computing and Secure Edge Intelligence -- 3.1 Edge Computing -- 3.2 Key Advances in Edge Computing -- 3.2.1 Security -- 3.2.2 Reliability -- 3.2.3 Survivability -- 3.3 Secure Edge Intelligence -- 3.3.1 Background and Motivation -- 3.3.2 Design of Detection Module -- 3.3.2.1 Data Pre‐processing -- 3.3.2.2 Model Learning -- 3.3.2.3 Model Updating -- 3.3.3 Challenges Against Poisoning Attacks -- 3.4 Summary -- References -- Chapter 4 Edge Intelligence for Intrusion Detection -- 4.1 Edge Cyberinfrastructure -- 4.2 Edge AI Engine -- 4.2.1 Feature Engineering -- 4.2.2 Model Learning -- 4.2.3 Model Update -- 4.2.4 Predictive Analytics -- 4.3 Threat Intelligence -- 4.4 Preliminary Study -- 4.4.1 Dataset -- 4.4.2 Environmental Setup -- 4.4.3 Performance Evaluation -- 4.4.3.1 Computational Efficiency -- 4.4.3.2 Prediction Accuracy -- 4.5 Summary -- References -- Chapter 5 Robust Intrusion Detection -- 5.1 Preliminaries -- 5.1.1 Median Absolute Deviation -- 5.1.2 Mahalanobis Distance -- 5.2 Robust Intrusion Detection -- 5.2.1 Problem Formulation -- 5.2.2 Step 1: Robust Data Pre‐processing -- 5.2.3 Step 2: Bagging for Labeled Anomalies -- 5.2.4 Step 3: One‐class SVM for Unlabeled Samples -- 5.2.4.1 One‐class Classification -- 5.2.4.2 Algorithm of Optimal Sampling Ratio Section -- 5.2.5 Step 4: The Final Classifier -- 5.3 Experimental and Evaluation -- 5.3.1 Experiment Setup -- 5.3.1.1 Datasets -- 5.3.1.2 Environmental Setup -- 5.3.1.3 Evaluation Metrics -- 5.3.2 Performance Evaluation -- 5.3.2.1 Step 1 -- 5.3.2.2 Step 2 -- 5.3.2.3 Step 3 -- 5.3.2.4 Step 4 -- 5.4 Summary -- References. Chapter 6 Efficient Pre‐processing Scheme for Anomaly Detection -- 6.1 Efficient Anomaly Detection -- 6.1.1 Related Work -- 6.1.2 Principal Component Analysis -- 6.2 Proposed Pre‐processing Scheme for Anomaly Detection -- 6.2.1 Robust Pre‐processing Scheme -- 6.2.2 Real‐Time Processing -- 6.2.3 Discussion -- 6.3 Case Study -- 6.3.1 Description of the Raw Data -- 6.3.1.1 Dimension -- 6.3.1.2 Predictors -- 6.3.1.3 Response Variables -- 6.3.2 Experiment -- 6.3.3 Results -- 6.4 Summary -- References -- Chapter 7 Privacy Preservation in the Era of Big Data -- 7.1 Privacy Preservation Approaches -- 7.1.1 Anonymization -- 7.1.2 Differential Privacy -- 7.1.3 Federated Learning -- 7.1.4 Homomorphic Encryption -- 7.1.5 Secure Multi‐party Computation -- 7.1.6 Discussion -- 7.2 Privacy‐Preserving Anomaly Detection -- 7.2.1 Literature Review -- 7.2.2 Preliminaries -- 7.2.2.1 Bilinear Groups -- 7.2.2.2 Asymmetric Predicate Encryption -- 7.2.3 System Model and Security Model -- 7.2.3.1 System Model -- 7.2.3.2 Security Model -- 7.3 Objectives and Workflow -- 7.3.1 Objectives -- 7.3.2 Workflow -- 7.4 Predicate Encryption‐Based Anomaly Detection -- 7.4.1 Procedures -- 7.4.2 Development of Predicate -- 7.4.3 Deployment of Anomaly Detection -- 7.5 Case Study and Evaluation -- 7.5.1 Overhead -- 7.5.2 Detection -- 7.6 Summary -- References -- Chapter 8 Adversarial Examples: Challenges and Solutions -- 8.1 Adversarial Examples -- 8.1.1 Problem Formulation in Machine Learning -- 8.1.2 Creation of Adversarial Examples -- 8.1.3 Targeted and Non‐targeted Attacks -- 8.1.4 Black‐box and White‐box Attacks -- 8.1.5 Defenses Against Adversarial Examples -- 8.2 Adversarial Attacks in Security Applications -- 8.2.1 Malware -- 8.2.2 Cyber Intrusions -- 8.3 Case Study: Improving Adversarial Attacks Against Malware Detectors -- 8.3.1 Background. 8.3.2 Adversarial Attacks on Malware Detectors -- 8.3.3 MalConv Architecture -- 8.3.4 Research Idea -- 8.4 Case Study: A Metric for Machine Learning Vulnerability to Adversarial Examples -- 8.4.1 Background -- 8.4.2 Research Idea -- 8.5 Case Study: Protecting Smart Speakers from Adversarial Voice Commands -- 8.5.1 Background -- 8.5.2 Challenges -- 8.5.3 Directions and Tasks -- 8.6 Summary -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910632494403321 |
|
Xu Shengjie
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Privacy Games / / by Lei Xu, Chunxiao Jiang, Yi Qian, Yong Ren
| Data Privacy Games / / by Lei Xu, Chunxiao Jiang, Yi Qian, Yong Ren |
| Autore | Xu Lei |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (187 pages) |
| Disciplina | 005.73 |
| Soggetto topico |
Data structures (Computer science)
Data mining Information storage and retrieval Management information systems Computer science E-commerce Data Structures and Information Theory Data Mining and Knowledge Discovery Information Storage and Retrieval Management of Computing and Information Systems e-Commerce/e-business |
| ISBN | 3-319-77965-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 The Conflict between Big Data and Individual Privacy -- 2 Privacy-Preserving Data Collecting: A Simple Game Theoretic Approach -- 3 Contract-based Private Data Collecting -- 4 Dynamic Privacy Pricing -- 5 User Participation Game in Collaborative Filtering -- 6 Privacy-Accuracy Trade-off in Distributed Data Mining -- 7 Conclusion. |
| Record Nr. | UNINA-9910299269703321 |
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Xu Lei
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Developing Networks using Artificial Intelligence / / by Haipeng Yao, Chunxiao Jiang, Yi Qian
| Developing Networks using Artificial Intelligence / / by Haipeng Yao, Chunxiao Jiang, Yi Qian |
| Autore | Yao Haipeng |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (256 pages) |
| Disciplina | 006.3 |
| Collana | Wireless Networks |
| Soggetto topico |
Wireless communication systems
Mobile communication systems Artificial intelligence Computer communication systems Wireless and Mobile Communication Artificial Intelligence Computer Communication Networks |
| ISBN | 3-030-15028-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface vii -- Acknowledgements ix -- Table of Contents xi -- Chapter 1 Introduction 1 -- Chapter 2 Intelligence-Driven Networking Architecture 13 -- Chapter 3 Intelligent Network Awareness 31 -- Chapter 4 Intelligent Network Control 79 -- Chapter 5 Intelligent Network Resource Management 151 -- Chapter 6 Intention Based Networking Management 191 -- Chapter 7 Conclusions and Future Challenges 237 -- Index 241. |
| Record Nr. | UNINA-9910337605103321 |
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Yao Haipeng
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 2011 3rd Workshop on Data Center-Converged and Virtual Ethernet Switching : DC-CaVES 2011 : San Francisco, CA, USA, 9 September 2011
| Proceedings of the 2011 3rd Workshop on Data Center-Converged and Virtual Ethernet Switching : DC-CaVES 2011 : San Francisco, CA, USA, 9 September 2011 |
| Autore | Recio Renato J |
| Pubbl/distr/stampa | [Place of publication not identified], : ITC Press, 2011 |
| Descrizione fisica | 1 online resource (69 pages) |
| Collana | ACM Other conferences |
| Soggetto topico |
Electrical & Computer Engineering
Engineering & Applied Sciences Telecommunications |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Proceedings of the 3rd Workshop on Data Center - Converged and Virtual Ethernet Switching |
| Record Nr. | UNINA-9910376198403321 |
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Recio Renato J
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| [Place of publication not identified], : ITC Press, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 2011 International Workshop on Modeling, Analysis, and Control of Complex Networks : Cnet 2011 : San Francisco, CA, USA, 9 September 2011
| Proceedings of the 2011 International Workshop on Modeling, Analysis, and Control of Complex Networks : Cnet 2011 : San Francisco, CA, USA, 9 September 2011 |
| Autore | Van Mieghem Piet |
| Pubbl/distr/stampa | [Place of publication not identified], : ITC Press, 2011 |
| Descrizione fisica | 1 online resource (57 pages) |
| Collana | ACM Other conferences |
| Soggetto topico |
Electrical & Computer Engineering
Engineering & Applied Sciences Telecommunications |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Proceedings of the 2011 International Workshop on Modeling, Analysis, and Control of Complex Networks : Cnet 2011 : San Francisco, California, United States of America, 9 September 2011
Proceedings of the 2011 International Workshop on Modeling, Analysis, and Control of Complex Networks |
| Record Nr. | UNINA-9910376198603321 |
|
Van Mieghem Piet
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| [Place of publication not identified], : ITC Press, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Resource Management for Heterogeneous Networks in LTE Systems / / by Rose Qingyang Hu, Yi Qian
| Resource Management for Heterogeneous Networks in LTE Systems / / by Rose Qingyang Hu, Yi Qian |
| Autore | Hu Rose Qingyang |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (90 p.) |
| Disciplina | 621.38456 |
| Collana | SpringerBriefs in Electrical and Computer Engineering |
| Soggetto topico |
Electrical engineering
Computer communication systems Power electronics Application software Communications Engineering, Networks Computer Communication Networks Power Electronics, Electrical Machines and Networks Information Systems Applications (incl. Internet) |
| ISBN | 1-4939-0372-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Heterogeneous Network Model and Preliminaries -- Mobile Association for Heterogeneous Networks -- Interference Management in Heterogeneous Networks with Fractional Frequency Reuse -- Radio Resource Allocation in Heterogeneous Networks. |
| Record Nr. | UNINA-9910299477703321 |
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Hu Rose Qingyang
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| New York, NY : , : Springer New York : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Terrestrial-Satellite Communication Networks : Transceivers Design and Resource Allocation / / by Linling Kuang, Chunxiao Jiang, Yi Qian, Jianhua Lu
| Terrestrial-Satellite Communication Networks : Transceivers Design and Resource Allocation / / by Linling Kuang, Chunxiao Jiang, Yi Qian, Jianhua Lu |
| Autore | Kuang Linling |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIII, 151 p. 72 illus., 51 illus. in color.) |
| Disciplina | 621.3825 |
| Collana | Wireless Networks |
| Soggetto topico |
Electrical engineering
Computer communication systems Communications Engineering, Networks Computer Communication Networks |
| ISBN | 3-319-61768-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 2 Beamforming Transmission -- 3 Interference Cancelation Reception -- 4 Spectrum Sharing -- 5 Spectrum Sensing -- 6 Multiple Access Resource Allocation -- 7 Conclusions and Future Challenges. |
| Record Nr. | UNINA-9910299874103321 |
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Kuang Linling
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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