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AI Embedded Assurance for Cyber Systems
| AI Embedded Assurance for Cyber Systems |
| Autore | Wang Cliff |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2023 |
| Descrizione fisica | 1 online resource (252 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
IyengarS. S
SunKun |
| Soggetto topico |
Computer security
Digital forensic science |
| ISBN | 3-031-42637-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- Contributors -- Acronyms -- Part I AI/ML for Digital Forensics -- 1 Writer-Dependent Off-Line Signature Verification with Neural Networks -- 1.1 Introduction -- 1.2 A Description of the Verification System -- 1.3 Preprocessing -- 1.3.1 Loading the Image and Conversion to Grayscale -- 1.3.2 Cropping the Image -- 1.3.3 Binarization -- 1.3.4 Resizing the Image -- 1.3.5 Thinning (Skeletonization) -- 1.4 Feature Extraction -- 1.4.1 Global Features -- 1.4.1.1 Number of Signature Pixels (in Thinned Image) -- 1.4.1.2 Area of the Signature (in Resized Image) -- 1.4.1.3 Aspect Ratio (in Binary Image) -- 1.4.1.4 The Inclination Angle of the Line Joining the Center of Gravity and the Lower Right Corner (in Binary Image) -- 1.4.1.5 Vertical Variance and Horizontal Variance (in Binary Image) -- 1.4.1.6 Intersection Points and Border Points (in Thinned Image) -- 1.4.2 Local Features -- 1.4.2.1 Pixel Density -- 1.4.2.2 Pixel Angle -- 1.4.2.3 Pixel Distance -- 1.5 Pairing and Classification -- 1.5.1 Pairing -- 1.5.2 Classification -- 1.5.2.1 NN Architecture -- 1.5.2.2 Training NN -- 1.6 Verification Process -- 1.7 Experiment Results -- 1.7.1 ICDAR 2011 SigComp Database -- 1.7.2 GPDS Synthetic Signature Corpus -- 1.7.3 Experiment Results -- 1.7.3.1 Evaluation Metrics -- 1.7.3.2 ICDAR 2011 Database Test Results -- 1.7.3.3 GPDS Corpus Test Results -- 1.8 Summary and Future Works -- References -- 2 Political Activism and Technology -- 2.1 Activism and Technology Use -- 2.2 Threat Models and the Technical-Defensive Landscape -- 2.2.1 Shaping Threat Models Through the Technical Capabilities of Political Allies and Enemies -- 2.2.2 The Power of the State to Compel Authentication -- 2.2.3 Control over the Telecommunication Infrastructure -- 2.3 Societal Context and Technology Adoption.
2.3.1 Institutional Knowledge Sharing-Security and Privacy Advice -- 2.3.2 Building Trust in a Mutating Group Surrounded by Uncertainty -- 2.3.3 Support From Abroad -- 2.4 Conclusions-Needs and Technology -- References -- 3 Forensic Proof and Criminal Liability for Development, Distribution and Use of Artificial Intelligence -- 3.1 Artificial Intelligence and Criminal Liability -- 3.2 Injuries as Crimes, from Loss of Life to Loss of Liberties, and Policies of Review -- 3.3 Investigation, AI Forensics and Proof of Responsibility -- 3.4 The Ethics of Artificial Intelligence -- 3.5 Conclusion -- References -- Part II AI/ML for CPS -- 4 Automotive Batteries as Anomaly Detectors -- 4.1 Introduction -- 4.2 Prototpe and Data Collection -- 4.3 Case-Study: Detecting Engine Anomalies Using Batteries -- 4.3.1 Automotive Battery and Vehicle Engine -- 4.3.2 Detecting RPM Anomalies with Battery -- 4.3.2.1 Data Preparation -- 4.3.2.2 Norm Model Construction -- 4.3.2.3 Anomaly Detection -- 4.3.2.4 Anomaly Verification -- 4.4 Detecting Vehicle Anomaliues Beyond Enginen RPM -- 4.5 Evaluations -- 4.5.1 B-Diag Against ``True'' Anomalies -- 4.5.1.1 Methodology -- 4.5.1.2 Evaluation Results -- 4.5.1.3 Adapter Faults or Vehicle Faults? -- 4.5.2 B-Diag Against Emulated Anomalies -- 4.5.2.1 Anomaly Model -- 4.5.2.2 Evaluation with Subaru Crosstrek -- 4.5.2.3 Evaluation with Other Vehicles -- 4.5.2.4 Diagnosing Beyond Engine RPM -- 4.6 Conclusions -- References -- 5 Zero Trust Architecture For Cyber-Physical Power System Security Based on Machine Learning -- 5.1 Introduction -- 5.2 Overview of Cyber-Physical Power System Security -- 5.2.1 The Hierarchical Structure for Cyber-Physical Power System -- 5.2.2 Cyber-Physical Power System Security -- 5.2.3 Examples for Cross-layer Failures in CPPS -- 5.3 Machine Learning Application in Cyber-Physical Power System Security. 5.3.1 Challenges in Model-Based Approaches for CPPS Security -- 5.3.2 Machine Learning Approaches for CPPS Security -- 5.4 A Combination of Novel Security Technique and Machine Learning-Based Approaches -- 5.4.1 Zero Trust Architecture Basics -- 5.4.1.1 Variations of Zero Trust Architecture Techniques -- 5.4.1.2 Related Work and Research Gap -- 5.4.2 Zero Trust Architecture Dedicated to Cyber-Physical Power System -- 5.4.3 Dynamic Trust Evaluation in Score-Based Policy Engine -- 5.4.3.1 Measurement Recovery in State Estimation -- 5.4.3.2 Shedding Loads in Frequency Recovery -- 5.4.4 How Machine Learning Empowers Zero Trust Architecture -- 5.4.4.1 User and Entity Behavioral Analytics Based on ML -- 5.4.4.2 Dynamic Access Control with at Least Privilege -- 5.5 Conclusion -- References -- 6 AI-enabled Real-Time Sensor Attack Detection for Cyber-Physical Systems -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Cyber-Physical Systems -- 6.2.2 Sensor Attacks in CPSs -- 6.3 AI-enabled Sensor Attack Detection -- 6.3.1 Sensor Attack Detection Tasks -- 6.3.2 Workflow for AI-based Detector Design -- 6.3.3 Neural Network Model Selection -- 6.3.4 Case Studies -- 6.4 Detection Exploring Inherent Sensor Redundancy -- 6.4.1 Background of Auto-Encoder -- 6.4.2 Methodology -- 6.4.2.1 How to Forward Sensor Data to Autoencoder -- 6.4.2.2 Reconstruction Error Measurement -- 6.4.2.3 Threshold Estimation -- 6.4.3 Results -- 6.4.3.1 Experimental Setup -- 6.4.3.2 Performance Analysis -- 6.5 Real-Time Adaptive Sensor Attack Detection -- 6.5.1 System Design -- 6.5.1.1 Attack Detector -- 6.5.1.2 Behavior Predictor -- 6.5.1.3 Design of Drift Adaptor -- 6.5.2 Results -- 6.6 Conclusion -- References -- Part III AI/ML for Cyber Analysis -- 7 Generating Vulnerable Code via Learning-Based Program Transformations -- 7.1 Introduction -- 7.2 Mining Existing Vulnerability Data. 7.3 Learning-Based Data Generation via Vulnerability Injection -- 7.3.1 Problem Formulation -- 7.3.2 Model Design -- 7.3.3 Model Training and Testing -- 7.4 Technique Implementation -- 7.5 Empirical Evaluation of Performance -- 7.5.1 Experimental Setup -- 7.5.2 Evaluation Results -- 7.6 Discussion -- 7.6.1 Data Characteristics -- 7.6.2 Technical Limitations -- 7.7 Conclusion -- References -- 8 10 Security and Privacy Problems in Large Foundation Models -- 8.1 Introduction -- 8.2 Background on Self-Supervised Learning -- 8.2.1 Self-Supervised Learning in NLP -- 8.2.1.1 Pre-Training a Language Model -- 8.2.1.2 Fine-Tuning a Language Model for a Downstream Task -- 8.2.2 Self-Supervised Learning in CV -- 8.2.2.1 Pre-Training an Image Encoder (and a Text Encoder) -- 8.2.2.2 Applying an Image Encoder (and a Text Encoder) to Downstream Tasks -- 8.2.3 Self-Supervised Learning in Graph -- 8.2.3.1 Pre-Training a Graph Encoder -- 8.2.3.2 Applying a Graph Encoder to Downstream Tasks -- 8.3 Six Problems on Confidentiality -- 8.4 Three Problems on Integrity -- 8.5 One Problem on Availability -- 8.6 Conclusion -- References -- 9 Federated Learning for IoT Applications, Attacks and Defense Methods -- 9.1 Introduction -- 9.2 Background of Federated Learning -- 9.3 Internet of Things FL -- 9.3.1 General Applications of IoT FL -- 9.3.2 Secure Applications of IoT FL -- 9.4 Threat Models in FL -- 9.4.1 Outside Attacks in FL -- 9.4.2 Inside Attacks in FL -- 9.5 Defense Methods Against Attacks in FL -- 9.5.1 Defense Methods Against Adversarial Attacks in FL -- 9.5.2 Defense Methods Against Privacy Attacks in FL -- 9.6 Conclusion -- References -- 10 AI Powered Correlation Technique to Detect Virtual Machine Attacks in Private Cloud Environment -- 10.1 Introduction -- 10.2 Related Works -- 10.3 CORRNET: Correlational Neural Network -- 10.3.1 CorrNet Implementation. 10.3.2 Analysis of CorNet -- 10.3.3 Classification of Data -- 10.3.4 Detection -- 10.3.5 Correlation -- 10.4 Threat Prediction and Protection Algorithm -- 10.4.1 Technique Customization -- 10.4.2 Implementation of Algorithmic Structure -- 10.5 Case Study: Digital Payment Service -- 10.6 Conclusion -- References -- 11 Detecting Fake Users in Online Social Networks -- 11.1 Introduction -- 11.2 Related Work -- 11.2.1 The Behavior-Based Sybil Attack Detection -- 11.2.2 The Structure-Based Sybil Attack Detection -- 11.3 Detect Sybil Accounts at Registration and Growing-Up Stages -- 11.3.1 Overview -- 11.3.2 Detecting Sybil Accounts Using the Registration Information -- 11.3.2.1 Feature Extraction -- 11.3.2.2 Registration Graph Building -- 11.3.2.3 Sybil Accounts Detection -- 11.3.3 Detecting Sybil Accounts with Growing-Up Behaviors -- 11.3.3.1 Account-Behavior Bigraph Construction -- 11.3.3.2 Account-Account Graph Construction -- 11.3.3.3 Unsupervised Maliciousness Assessment -- 11.3.3.4 Growing-Up Sybil Account Detection -- 11.4 New Challenges in Defending Sybil Attack -- 11.5 Conclusion -- References -- 12 Explaining Deep Learning Based Security Applications -- 12.1 Introduction -- 12.2 Explainable Machine Learning -- 12.2.1 Problem Definition -- 12.2.2 Whitebox Explanation Methods -- 12.2.3 Blackbox Explanation Methods -- 12.3 Explaining Security Applications -- 12.3.1 Deep Learning in Security Applications -- 12.3.2 Why Not Existing Explanation Methods -- 12.4 Our Explanation Method -- 12.4.1 Insights Behind Our Designs -- 12.4.2 Model Development -- 12.4.3 Applying the Model for Explanation -- 12.5 Evaluation -- 12.5.1 Experimental Setup -- 12.5.2 Fidelity Evaluation -- 12.5.3 Experimental Results -- 12.6 Applications of ML Explanation -- 12.6.1 Understanding Classifier Behavior -- 12.6.2 Troubleshooting Classification Errors. 12.6.3 Targeted Patching of ML Classifiers. |
| Record Nr. | UNINA-9910770267403321 |
Wang Cliff
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| Cham : , : Springer International Publishing AG, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning Networks : Design, Development and Deployment / / by Jayakumar Singaram, S. S. Iyengar, Azad M. Madni
| Deep Learning Networks : Design, Development and Deployment / / by Jayakumar Singaram, S. S. Iyengar, Azad M. Madni |
| Autore | Singaram Jayakumar |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (173 pages) |
| Disciplina | 621.382 |
| Altri autori (Persone) |
IyengarS. S
MadniAzad M |
| Soggetto topico |
Telecommunication
Machine learning Computational intelligence Pattern recognition systems Communications Engineering, Networks Machine Learning Computational Intelligence Automated Pattern Recognition |
| ISBN | 3-031-39244-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Deep Learning -- Brief survey on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) -- Tool Set for Deep Learning Applications -- Data-Set Design and Data Labeling -- DL Model: Design and Development -- Training and Testing of DL Model -- Deploying DL in Jetson Nano -- Deploying DL in Android Phone -- Deploying DL in Ultra96-V2 Field Programmable Gate Array (FPGA) -- Conclusion. |
| Record Nr. | UNINA-9910760290003321 |
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Singaram Jayakumar
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematical Theories of Machine Learning - Theory and Applications / / by Bin Shi, S. S. Iyengar
| Mathematical Theories of Machine Learning - Theory and Applications / / by Bin Shi, S. S. Iyengar |
| Autore | Shi Bin |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXI, 133 p. 25 illus., 24 illus. in color.) |
| Disciplina |
621.382
006.310151 |
| Soggetto topico |
Electrical engineering
Computational intelligence Data mining Information storage and retrieval Big data Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Big Data/Analytics |
| ISBN | 3-030-17076-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. General Framework of Mathematics -- Chapter 3. Problem Formulation -- Chapter 4. Development of Novel Techniques of CoCoSSC Method -- Chapter 5. Further Discussions of the Proposed Method -- Chapter 6. Related Work on Geometry of Non-Convex Programs -- Chapter 7. Gradient Descent Converges to Minimizers -- Chapter 8. A Conservation Law Method Based on Optimization -- Chapter 9. Improved Sample Complexity in Sparse Subspace Clustering with Noisy and Missing Observations -- Chapter 10. Online Discovery for Stable and Grouping Causalities in Multi-Variate Time Series -- Chapter 11. Conclusion. |
| Record Nr. | UNINA-9910366589703321 |
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Shi Bin
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modern Technologies Enabling Safe and Secure UAV Operation in Urban Airspace
| Modern Technologies Enabling Safe and Secure UAV Operation in Urban Airspace |
| Autore | Śniatała P |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | , : IOS Press, Incorporated, , 2021 |
| Descrizione fisica | 1 online resource (158 pages) |
| Disciplina | 629.13339 |
| Altri autori (Persone) |
IyengarS. S
BendarmaA |
| Collana | NATO Science for Peace and Security Series - d: Information and Communication Security |
| Soggetto topico | Drone aircraft--Control systems |
| ISBN | 1-64368-189-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910794523603321 |
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Śniatała P
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| , : IOS Press, Incorporated, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modern Technologies Enabling Safe and Secure UAV Operation in Urban Airspace
| Modern Technologies Enabling Safe and Secure UAV Operation in Urban Airspace |
| Autore | Śniatała P |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | , : IOS Press, Incorporated, , 2021 |
| Descrizione fisica | 1 online resource (158 pages) |
| Disciplina | 629.13339 |
| Altri autori (Persone) |
IyengarS. S
BendarmaA |
| Collana | NATO Science for Peace and Security Series - d: Information and Communication Security |
| Soggetto topico | Drone aircraft--Control systems |
| ISBN | 1-64368-189-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910827045603321 |
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Śniatała P
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| , : IOS Press, Incorporated, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Smart Grids: Security and Privacy Issues / / by Kianoosh G. Boroojeni, M. Hadi Amini, S. S. Iyengar
| Smart Grids: Security and Privacy Issues / / by Kianoosh G. Boroojeni, M. Hadi Amini, S. S. Iyengar |
| Autore | Boroojeni Kianoosh G |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIV, 113 p. 26 illus., 25 illus. in color.) |
| Disciplina | 621.382 |
| Soggetto topico |
Electrical engineering
Power electronics Computer security Computational intelligence Application software Communications Engineering, Networks Power Electronics, Electrical Machines and Networks Systems and Data Security Computational Intelligence Information Systems Applications (incl. Internet) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview of the Security and Privacy Issues in Smart Grids -- I Physical Network Security -- Reliability in Smart Grids -- Error Detection of DC Power Flow using State Estimation -- Bad Data Detection -- II Information Network Security -- Cloud Network Data Security -- III Privacy Preservation -- End-User Data Privacy -- Mobile User Data Privacy. |
| Record Nr. | UNINA-9910135973503321 |
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Boroojeni Kianoosh G
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sustainable Interdependent Networks II : From Smart Power Grids to Intelligent Transportation Networks / / edited by M. Hadi Amini, Kianoosh G. Boroojeni, S. S. Iyengar, Panos M. Pardalos, Frede Blaabjerg, Asad M. Madni
| Sustainable Interdependent Networks II : From Smart Power Grids to Intelligent Transportation Networks / / edited by M. Hadi Amini, Kianoosh G. Boroojeni, S. S. Iyengar, Panos M. Pardalos, Frede Blaabjerg, Asad M. Madni |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (316 pages) |
| Disciplina | 006.22068 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Electrical engineering
Power electronics Computational intelligence Application software Computer security Communications Engineering, Networks Power Electronics, Electrical Machines and Networks Computational Intelligence Information Systems Applications (incl. Internet) Systems and Data Security |
| ISBN | 3-319-98923-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- A System of Systems: Engineering Framework for Active Distribution Grids Operation -- Clustering Algorithms in Wireless Sensor Networks: Challenges, Solutions, and Future Research Trends -- Laboratory-Scale Microgrid System for Control of Power Distribution in Local Energy Networks -- Impact of Strategic Behavior of the Electrical Consumers on the Power System Reliability -- Reactive Power Dispatch Strategies for Loss Minimization in a DFIG based Wind Farm -- Distributed State Estimation and Energy Management in Smart Grids: A Consensus + Innovations Approach -- Promises of Intelligent Transportation Systems in Future Smart Cities -- High Performance and Scalable Graph Computation on GPUs for Smart Power Grids and Transportation System Applications -- A Comprehensive Review on Emerging Methods for Integration of Electric Vehicles into Power Systems -- A Comprehensive Overview of Distributed/Decentralized Control and Optimization Strategies of AC and DC Microgrids -- Hopf Bifurcation Control of Large-Scale Complex Nonlinear Dynamical Systems Via a Dynamic State Feedback Controller: The Tale of Power Networks -- Conclusion. |
| Record Nr. | UNINA-9910337469503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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