Cyber Security Meets Machine Learning

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