Cyber Security Meets Machine Learning

✍ Scribed by Xiaofeng Chen, Willy Susilo, Elisa Bertino

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 168
• Category: Library

✦ Synopsis

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users.

This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning.

Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

✦ Table of Contents

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