04953nam 2200481 450 99646448870331620220327091830.0981-336-726-1(CKB)4100000011979650(MiAaPQ)EBC6676429(Au-PeEL)EBL6676429(OCoLC)1259623724(PPN)260302511(EXLCZ)99410000001197965020220327d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCyber security meets machine learning /Xiaofeng Chen, Willy Susilo, Elisa BertinoSingapore :Springer,[2021]©20211 online resource (168 pages)981-336-725-3 Intro -- Preface -- Contents -- IoT Attacks and Malware -- 1 Introduction -- 2 Background -- 2.1 Cybersecurity Kill Chains -- 2.2 Major IoT Security Concerns -- 3 Attack Classification -- 3.1 Passive/Information Stealing Attacks -- 3.2 Service Degradation Attacks -- 3.3 DDoS Attacks -- 4 IoT Malware Analysis and Classification -- 5 AI-Based IDS Solutions -- 6 Conclusion -- References -- Machine Learning-Based Online Source Identification for Image Forensics -- 1 Introduction -- 2 Related Work -- 2.1 Features Engineering for Image Source Identification -- 2.2 Statistical Learning-Based Image Source Identification -- 3 Proposed Scheme: OSIU -- 3.1 Unknown Sample Triage -- 3.2 Unknown Image Discovery -- 3.3 (K+1)-class Classification -- 4 Experiments and Results -- 4.1 Dataset and Experiment Settings -- 4.2 Features -- 4.3 Evaluation Metrics -- 4.4 Performance of Triaging Unknown Samples -- 4.5 Performance of OSIU -- 5 Conclusion -- References -- Reinforcement Learning Based Communication Security for Unmanned Aerial Vehicles -- 1 Introduction -- 2 Communication Security for Unmanned Aerial Vehicles -- 2.1 UAV Communication Model -- 2.2 Attack Model -- 3 Reinforcement Learning Based UAV Communication Security -- 3.1 Reinforcement Learning Based Anti-Jamming Communications -- 3.2 Reinforcement Learning Based UAV Communications Against Smart Attacks -- 4 UAV Secure Communication Game -- 4.1 Game Model -- 4.2 Nash Equilibrium of the Game -- 5 Related Work -- 5.1 General Anti-jamming Policies in UAV-Aided Communication -- 5.2 Reinforcement Learning in Anti-jamming Communication -- 5.3 Game Theory in Anti-jamming Communication -- 6 Conclusion -- References -- Visual Analysis of Adversarial Examples in Machine Learning -- 1 Introduction -- 2 Adversarial Examples -- 3 Generation of Adversarial Examples -- 4 Properties of Adversarial Examples.5 Distinguishing Adversarial Examples -- 6 Robustness of Models -- 7 Challenges and Research Directions -- 8 Conclusion -- References -- Adversarial Attacks Against Deep Learning-Based Speech Recognition Systems -- 1 Introduction -- 2 Background and Related Work -- 2.1 Speech Recognition -- 2.2 Adversarial Examples -- 2.3 Related Work -- 3 Overview -- 3.1 Motivation -- 3.2 Technical Challenges -- 4 White-Box Attack -- 4.1 Threat Model of White-Box Attack -- 4.2 The Detail Decoding Process of Kaldi -- 4.3 Gradient Descent to Craft Audio Clip -- 4.4 Practical Adversarial Attack Against White-Box Model -- 4.5 Experiment Setup of CommanderSong Attack -- 4.6 Evaluation of CommanderSong Attack -- 5 Black-Box Attack -- 5.1 Threat Model of Black-Box Attack -- 5.2 Transferability Based Approach -- 5.3 Local Model Approximation Approach -- 5.4 Alternate Models Based Generation Approach -- 5.5 Experiment Setup of Devil's Whisper Attack -- 5.6 Evaluation of Devil's Whisper Attack -- 6 Defense -- 7 Conclusion -- Appendix -- References -- A Survey on Secure Outsourced Deep Learning -- 1 Introduction -- 2 Deep Learning -- 2.1 Brief Survey on Deep Learning -- 2.2 Architecture of Deep Learning -- 2.3 Main Computation in Deep Learning -- 3 Outsourced Computation -- 3.1 Brief Survey on Outsourced Computation -- 3.2 System Model -- 3.3 Security Requirements -- 4 Outsourced Deep Learning -- 4.1 Brief Review on Outsourced Deep Learning -- 4.2 Privacy Concerns in Outsourced Deep Learning -- 4.3 Privacy-Preserving Techniques for Outsourced Deep Learning -- 4.4 Taxonomy Standard -- 4.5 Privacy-Preserving Training Outsourcing -- 4.6 Privacy-Preserving Inference Outsourcing -- 5 Conclusion and Future Research Perspectives -- References.ss.Machine learningTechniqueMachine learningSecurity measuresMachine learningTechnique.Machine learningSecurity measures.006.31Chen Xiaofeng850517Susilo WillyBertino ElisaMiAaPQMiAaPQMiAaPQBOOK996464488703316Cyber security meets machine learning2814028UNISA01808nam 2200517 450 991015469640332120230807205921.01-61312-753-7(CKB)3710000000571291(EBL)4013039(SSID)ssj0001671283(PQKBManifestationID)16460598(PQKBTitleCode)TC0001671283(PQKBWorkID)14671623(PQKB)11586324(MiAaPQ)EBC4013039(MiAaPQ)EBC4011781(EXLCZ)99371000000057129120170111h20152015 uy 1engur|n|---|||||txtccrThe rise and fall of the Gallivanters a novel /by M. J. BeaufrandNew York, New York :Amulet Books,2015.©20151 online resource (159 p.)Description based upon print version of record.1-4197-1495-3 In 1983, a band led by a David Bowie lookalike prepares to compete in a battle of the bands at a possibly evil brewery, and must also cope with the fact that bass player Evan is getting sicker and sicker, much like his best friend Noah's abusive father did before his death several years earlier.Friendship in adolescenceFriendship in adolescenceJuvenile fictionBands (Music)Juvenile fictionFriendship in adolescence.Friendship in adolescenceBands (Music)158.25Beaufrand Mary Jane1229330MiAaPQMiAaPQMiAaPQBOOK9910154696403321The rise and fall of the Gallivanters2853478UNINA