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Title Deep learning applications for cyber security / Mamoun Alazeb, MingJian Tang, editors
Published Cham, Switzerland : Springer, [2019]
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Description 1 online resource (260 pages)
Series Advanced sciences and technologies for security applications, 2363-9466
Advanced sciences and technologies for security applications
Contents Intro; Foreword; Acknowledgements; Contents; About the Authors; Adversarial Attack, Defense, and Applications with Deep Learning Frameworks; 1 Introduction; 2 Methods for Generating Adversarial Samples; 2.1 Box-Constrained L-BFGS Approach; 2.2 Fast Gradient Sign Method; 2.3 Iterative Gradient Sign Methods; 2.4 Jacobian-Based Saliency Map Attack; 2.5 DeepFool; 2.6 Carlini and Wagner Attack; 2.7 Transferability Based Approach; 2.8 Houdini; 3 Methods for Defending Adversarial Attacks; 3.1 Adversarial Training; 3.2 Defensive Distillation; 3.3 Game Theory Based Approach; 3.4 MagNet; 3.5 Defense-GAN
3.1.3 Unsupervised Deep Representation Learning with Generative Models3.2 Video-Based Person Re-identification; 4 Conclusions; 5 Open Problems; Problems; Problem 1; Problem 2; References; Deep Learning-Based Detection of Electricity Theft Cyber-Attacks in Smart Grid AMI Networks; 1 Introduction; 2 Review of State-of-the-Art Detection Techniques; 2.1 Hardware-Based Solutions; 2.2 Software-Based Solutions; 3 Dataset Used and Cyber Attacks Injection; 3.1 Energy Consumption Dataset; 3.2 Electricity Theft Cyber-Attacks; 4 Proposed Deep Electricity Theft Detectors
4 Adversarial Learning Applications for Cyber Security4.1 Attack Commercial Web Services; 4.2 Attack Automatic Speech Recognition System; 4.3 Attack Malware Classifier; 5 Conclusion; References; Intelligent Situational-Awareness Architecture for Hybrid Emergency Power Systems in More Electric Aircraft; 1 Introduction; 2 Problem Setting; 3 Situation-Aware Intelligent Security Control Architecture for Energy Management Strategy; 3.1 Deep Learning-Based Cyber Attack Detection Scheme; 3.2 Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Estimation Scheme; 4 Simulation Results
4.1 Deep Customer-Specific Electricity Theft Detector4.2 Feedforward Detector; 4.3 Gated Recurrent Detector; 4.4 Deep Generalized Electricity Theft Detector; 5 Hyper-Parameters Tunning; 5.1 Sequential Grid Search Tunning for Hyper-Parameters; 5.2 Random Grid Search Tunning for Hyper-Parameters; 5.3 Genetic Algorithm-Based Tunning for Hyper-Parameters; 6 Numerical Results and Discussion; 6.1 Customer-Specific Detector Evaluation with Sequential Grid Search for Hyper-Parameters Tuning; 6.2 Generalized Detector Evaluation with Random Grid Search for Hyper-Parameters Tuning
4.1 Deep Learning Implementation and Results4.2 ANFIS Implementation and Results; 5 Conclusion; References; Deep Learning in Person Re-identification for Cyber-Physical Surveillance Systems; 1 Introduction; 2 Background; 2.1 Supervised Learning in Person Re-identification; 2.2 Unsupervised Learning in Person Re-identification; 3 Deep Learning in Person Re-identification for Cyber-Physical Surveillance Systems: New Trending Methodologies; 3.1 Image-Based Person Re-identification; 3.1.1 Supervised Deep Representation Learning; 3.1.2 Deep Hashing Learning for Fast Person Re-identification
Summary Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points
Notes 6.3 Generalized Detector Evaluation with Genetic-Based Search for Hyper-Parameters Tuning
Bibliography Includes bibliographical references
Notes Print version record
Subject Computer security
Computer security.
Form Electronic book
Author Alazab, Mamoun, editor
Tang, MingJian, editor
ISBN 3030130568
3030130576
3030130584
3030130592
9783030130565
9783030130572
9783030130589
9783030130596