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Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications
A Practical Guide for Students, Researchers, and Cybersecurity Professionals

Artificial Intelligence is rapidly becoming part of modern software, business operations, healthcare, finance, transportation, smart devices, and critical infrastructure. As AI systems become more capable and more widely deployed, securing these systems has become an increasingly important challenge.

Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications explores the intersection of Artificial Intelligence and Cybersecurity, providing readers with a structured understanding of how AI systems can be attacked, how AI can be misused, and how secure and trustworthy AI systems can be designed.

The book begins with the foundations of Artificial Intelligence and Cybersecurity, introducing AI concepts, common security principles, attack surfaces, cryptography, hashing, and traditional cybersecurity threats. It then moves into AI-specific security challenges such as data poisoning, training-time attacks, adversarial machine learning, model theft, model inversion, membership inference, and privacy risks.

Readers are introduced to important adversarial machine learning concepts and defensive approaches while learning why AI systems require security considerations throughout their entire lifecycle—from data collection and training to deployment, monitoring, and maintenance.

The book also examines the increasingly important topic of AI-enabled cyber threats, including AI-generated phishing, social engineering, deepfakes, synthetic identities, automated malicious activity, and other forms of technology-enabled abuse. These topics are discussed from a cybersecurity awareness and defensive perspective.

A major section of the book focuses on defending AI systems. Readers explore adversarial training, adversarial-input detection, secure data pipelines, model validation, versioning, monitoring, explainability, fairness, and trustworthy AI practices.

The book further introduces security and AI-related tools and frameworks, including TensorFlow Privacy, CleverHans, IBM Adversarial Robustness Toolbox, Wireshark, Metasploit, Kali Linux, and secure MLOps concepts, with emphasis on responsible security testing and defensive applications.

The later chapters explore the relationship between AI security and emerging technologies such as Blockchain, IoT, Edge Computing, and quantum-era security considerations. The book also addresses AI ethics, privacy, governance, compliance, risk management, and responsible red teaming.

Finally, readers are introduced to career opportunities in AI cybersecurity and emerging areas such as AI Security Engineering, ML Red Teaming, Trust & Safety, predictive threat intelligence, autonomous cyber defense, and security considerations for Generative AI systems.

Key Features

Foundations of AI and Cybersecurity
AI-specific attack surfaces and vulnerabilities
Data poisoning and training-time attacks
Adversarial Machine Learning
Model theft, inversion, and inference attacks
AI-enabled phishing and social engineering
Deepfakes and synthetic media risks
Defensive strategies for adversarial attacks
Secure AI lifecycle and MLOps
Explainable and trustworthy AI
AI security tools and frameworks
Blockchain, IoT, and AI security
Privacy, governance, and compliance
AI risk management frameworks
Ethical hacking and responsible red teaming
AI cybersecurity career pathways
Generative AI security and prompt-injection risks
Real-world case studies and practical security scenarios

The book is suitable for Computer Science and IT students, cybersecurity learners, AI/ML students, researchers, ethical security practitioners, developers, security professionals, educators, and technology enthusiasts who want to understand the security challenges associated with intelligent systems.

Rather than treating AI and cybersecurity as separate disciplines, this book presents them as increasingly interconnected fields and encourages readers to think about security throughout the design, development, deployment, and operation of AI systems.

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