Understanding AI in Cybersecurity and Secure AI Challenges, Strategies and Trends (Dilli Prasad Sharma, Arash Habibi Lashkari etc.)（Z-Library）

【One-Line Pitch】 A structured survey of how AI is applied across cybersecurity domains—and, just as importantly, how AI systems themselves must be secured against adversarial manipulation. Best suited to researchers, graduate students, and security practitioners who want a single map of both directions of the AI–security relationship. 【Book Arc】 - **Opening (~0%–15%)**: Frames the two-sided problem—AI as a security tool and AI as an attack surface—and situates the book within the authors' "Understanding Cybersecurity Series," establishing scope and audience. - **Early (~15%–35%)**: Lays the conceptual groundwork: the cyber threat landscape and attacker motivations, what AI is, the shift from traditional to AI-centric cybersecurity, and a primer on ML model families (supervised, unsupervised, semi-supervised, reinforcement) plus fundamental algorithms. - **Middle (~35%–50%)**: Applies AI domain by domain—network security (signature, anomaly, policy, and reputation-based detection), software/application security (smartphone, web, desktop, with web threats mapped to familiar vulnerability categories), cloud security (misconfiguration, unauthorized access, data breach, insecure APIs, insider threat), and IoT/OT security. - **Late (~50%–65%)**: Pivots to "secure AI": security requirements for AI systems, crafting adversarial samples, and a catalogue of defenses (adversarial training, certified robustness, gradient masking, input reconstruction, defensive distillation, ensembles, detection, verification). - **Ending (~65%+)**: Closes with privacy-preserving methods (anonymization, homomorphic encryption, federated learning, secure multi-party computation, differential privacy), explainability and interpretability, and forward-looking challenges and future work in secure AI. 【Key Takeaways】 - **The book treats AI and security as a two-way street** (Opening): AI is both a defensive/offensive tool for cybersecurity and a system that itself needs protection—this dual framing organizes the entire volume. - **Threat landscape comes before technique** (Early): attacker motivations and threat predictions are established first, so later AI applications read as responses to concrete problems rather than abstract methods. - **ML fundamentals are covered as a foundation, not a deep dive** (Early): supervised, unsupervised, semi-supervised, and reinforcement learning are surveyed alongside core algorithms to give readers shared vocabulary. - **AI-driven security is organized by domain** (Middle): network, application, cloud, and IoT/OT each get their own treatment, letting readers jump to the layer they operate in. - **Application security is split into smartphone, web, and desktop** (Middle): web threats are mapped to a recognizable vulnerability taxonomy (broken access control, injection, cryptographic failures, misconfiguration, and so on). - **Adversarial machine learning is the book's technical centerpiece** (Late): it covers both how adversarial samples are crafted and a broad menu of defenses, from adversarial training to certified robustness and network verification. - **Privacy and explainability are treated as first-class security requirements** (Late): federated learning, differential privacy, homomorphic encryption, and interpretability are presented as necessary complements to robustness. - **Challenges are named honestly rather than glossed over** (Ending): data diversity, model customization, explainability, and time complexity recur as open problems with suggested future work. 【Reading Tips】 - **Skim the ML primer if you already know it.** The early chapters on model families and fundamental algorithms are scaffolding; practitioners can move quickly to the domain chapters. - **Deep-read the secure-AI and adversarial sections.** These are the most distinctive and technically dense parts, and they carry the book's main argument. - **Use the domain chapters as reference, not narrative.** Network, application, cloud, and IoT/OT chapters are largely self-contained—read the one matching your environment first. - **Watch for the recurring "challenges and issues" subsections.** They signal where the field is unsettled and are useful for framing research or evaluation questions. - **Keep the two directions separate in your notes.** Track "AI for security" and "security for AI" as distinct threads; conflating them is the easiest way to lose the book's structure. 【Coverage Limits】 This guide is built from stratified excerpts that are heavy on front matter, tables of contents, and section headings, so it maps the book's structure and themes reliably but does not summarize the detailed content, examples, or findings inside individual chapters. Specific case studies, datasets, and quantitative results are not covered by the excerpts.