Machine Learning in Cyber Trust Security Privacy and Reliability 1st edition by Jeffrey Tsai, Philip Yu ISBN 0387887342 978-0387887340

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ISBN 10: 0387887342
ISBN 13: 978-0387887340
Author:  Jeffrey Tsai, Philip Yu

Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms.

This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significant area, and giving a classification of existing work.

Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.

Machine Learning in Cyber Trust: Security, Privacy, and Reliability 1st Table of contents:

• Introduction to Machine Learning and Cyber Trust

  • Overview of machine learning and its applications in cybersecurity.
  • Defining cyber trust, security, privacy, and reliability.
  • The role of machine learning in enhancing cybersecurity and privacy.

• Machine Learning Fundamentals for Cybersecurity

  • Introduction to machine learning techniques used in security (supervised, unsupervised, reinforcement learning).
  • Overview of data types and feature selection for machine learning in cybersecurity.
  • Evaluation metrics and model performance for security applications.

• Privacy-Preserving Machine Learning Techniques

  • Techniques for ensuring privacy in machine learning algorithms.
  • Differential privacy, federated learning, and homomorphic encryption.
  • Use cases for privacy-preserving machine learning in security contexts.

• Anomaly Detection and Intrusion Detection Systems

  • Machine learning models for detecting abnormal patterns in networks and systems.
  • Developing intrusion detection systems (IDS) using machine learning.
  • Case studies on the effectiveness of ML-based IDS.

• Threat Detection and Mitigation with Machine Learning

  • Using machine learning for malware detection and analysis.
  • Identifying phishing attacks, DDoS attacks, and other cyber threats.
  • Real-time threat detection using AI and machine learning.

• Security in Machine Learning Systems

  • Vulnerabilities specific to machine learning models and algorithms.
  • Adversarial attacks against machine learning models.
  • Methods for securing machine learning systems against attacks.

• Reliability and Robustness in Cybersecurity Systems

  • Building resilient machine learning models for cybersecurity.
  • Approaches to improving the robustness of machine learning systems in adversarial environments.
  • Balancing security, privacy, and reliability in cyber trust systems.

• Ethical and Legal Considerations in Machine Learning for Cybersecurity

  • Ethical issues surrounding data use, surveillance, and machine learning.
  • Legal implications of using machine learning in security and privacy.
  • Regulatory frameworks for AI and machine learning in cybersecurity.

• Applications of Machine Learning in Industrial Cybersecurity

  • Machine learning for securing critical infrastructure (e.g., IoT, SCADA systems).
  • Use cases in industrial control systems (ICS) and smart cities.
  • Challenges and solutions in applying machine learning in industrial settings.

• Future Trends and Challenges

  • The evolving role of machine learning in cybersecurity and privacy.
  • Emerging technologies in AI and their potential impact on cyber trust.
  • The future of machine learning-based cybersecurity systems.

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