Book

Description

Modern Network Intrusion Detection Systems (NIDS) often achieve remarkable accuracy on benchmark datasets, yet many fail when deployed in real-world environments due to inconsistent feature representations, dataset bias, and limited cross-domain generalization. This book presents a data-centric approach to intrusion detection, demonstrating how principled data engineering can significantly improve operational performance without relying on increasingly complex deep learning architectures. Drawing from the development of the Aegis DLP platform, the book introduces a complete pipeline for transforming heterogeneous cybersecurity datasets—including KDD Cup'99, UNSW-NB15, CICIDS 2018, and live packet captures—into a unified feature space suitable for real-time intrusion detection. Readers are guided through semantic feature alignment, statistical feature derivation, temporal behavioral modeling, and the design of scalable preprocessing pipelines capable of supporting both offline training and online inference. Beyond dataset engineering, the book explores the implementation of a real-time monitoring framework using Scapy, Python, Flask, and machine learning, validated through a controlled virtual laboratory consisting of Kali Linux, Metasploitable 2, and Windows-based packet capture. Multiple machine learning approaches are examined before presenting a lightweight Multi-Layer Perceptron optimized for operational deployment. Rather than focusing solely on achieving high benchmark accuracy, this work emphasizes cross-dataset generalization, real-world validation, reproducible feature engineering, and practical cybersecurity deployment. It is intended for researchers, cybersecurity professionals, postgraduate students, and AI practitioners seeking to bridge the gap between academic intrusion detection research and deployable enterprise security solutions.