Artificial Intelligence for Intrusion Detection Systems 1st Edition

Boldly reimagine network defense with Artificial Intelligence for Intrusion Detection Systems, 1st Edition by — a timely, practical guide that brings machine learning and deep-learning approaches to real-world cybersecurity challenges.

Discover clear, accessible explanations of core concepts — from anomaly detection and signature-based methods to supervised, unsupervised, and hybrid AI models — framed for security engineers, IT managers, researchers, and informed professionals. This volume walks readers through algorithm selection, feature engineering, model evaluation, and deployment strategies for both network-based (NIDS) and host-based (HIDS) intrusion detection.

Packed with actionable insight, the book examines performance metrics, data preprocessing, common datasets, and the trade-offs between detection accuracy and false positives. It also addresses operational considerations such as scalability, real-time inference, and integration with SIEM and SOAR platforms, making it ideal for enterprise teams in North America, Europe, Asia-Pacific, and beyond seeking robust cybersecurity solutions.

Readers will gain the confidence to design, implement, and evaluate AI-driven IDS tailored to their infrastructure and regulatory environment. Case-driven explanations and practical recommendations bridge academic theory and operational practice, helping you reduce dwell time, improve incident response, and prioritize threats more effectively.

Whether you’re building next-generation security operations or sharpening your expertise in threat detection, Artificial Intelligence for Intrusion Detection Systems, 1st Edition by is an essential, forward-looking resource. Order now to equip your team with the knowledge to defend networks against evolving cyber threats worldwide.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.