{ "item_title" : "Intelligent Threat Detection using AI", "item_author" : [" Ajit Singh "], "item_description" : "The central philosophy of Intelligent Threat Detection using AI is rooted in pragmatic execution: theory without application is a vulnerability. In the realm of cybersecurity, knowing how an algorithm functions mathematically is vastly different from knowing how to deploy it as a robust, scalable service that intercepts live malicious traffic. Therefore, the philosophy of this book dictates that every concept introduced must immediately be tied to a practical application. I view AI not as an abstract academic discipline, but as a functional tool-a core component of a larger engineering ecosystem. The text operates on the belief that actual learning occurs through building, failing, debugging, and ultimately deploying working software. By shifting the focus away from overwhelming mathematical proofs and directing it entirely toward software engineering, system architecture, and production deployment, this book empowers readers to create tangible, industry-relevant security solutions. Key Features 1. From Scratch to Production: This text covers the entire lifecycle. It does not stop at model training. It guides the reader through data collection, feature engineering, model selection, training, testing, building an API wrapper, containerization (Docker), deployment, and live monitoring. 2. Industry-Relevant Architectures: Features modern design patterns, including MLOps (Machine Learning Operations), SecOps integration, CI/CD pipelines, and scalable cloud deployments. 3. Simplest Practical Examples: Every chapter includes the easiest possible real-life examples to illustrate complex topics like neural networks, anomaly detection, and automated threat response. 4. Complete DIY Capstone Project: Chapter 10 is exclusively dedicated to a massive, fully functioning DIY application. It provides complete, line-by-line working code and step-by-step implementation instructions for an AI-powered security system. 5. Comprehensive Domain Coverage: Covers all necessary components, including network layers, endpoint agents, threat intelligence feeds, and automated response frameworks. 6. Latest Trends: Incorporates updated topics on adversarial AI, zero-day threat prediction, and automated malware sandboxing. Key Takeaways By completing this text, readers will acquire the following actionable skills: 1. Architectural Design: The ability to design end-to-end AI security architectures, understanding how data flows from network sensors to predictive models and finally to automated response mechanisms. 2. Practical AI Implementation: The skill to write clean, effective code for simple yet powerful machine learning and deep learning algorithms specifically tailored for threat detection. 3. Deployment and MLOps: Mastery over the deployment lifecycle. Readers will know how to package their AI solutions into production-ready containers and deploy them securely. 4. Real-time Threat Mitigation: The capacity to build applications that monitor network traffic and endpoint behavior in real-time, instantly identifying and neutralizing anomalies. 5. Project Portfolio: A fully working, live capstone project that serves as a testament to the reader's ability to build industry-relevant AI applications from scratch to final production. Disclaimer: Earnest request from the Author. Kindly go through the table of contents and refer kindle edition for a glance on the related contents. Thank you for your kind consideration", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/818/120/9798181205482_b.jpg", "price_data" : { "retail_price" : "31.28", "online_price" : "31.28", "our_price" : "31.28", "club_price" : "31.28", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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