Building the Hybrid Honeypot-Based ML-Driven IDS Architecture

This chapter presents a detailed framework that combines deception technologies and machine learning to develop an advanced intrusion detection system. Honeypots, placed at key network intersections, act as managed traps to gather genuine attack information, thus enhancing the quality and authenticity of training datasets. The architecture is characterized as multi-tiered: a collection tier for acquiring telemetry from honeypots and sensors; a processing tier for data normalization and enhancement; an intelligence tier that utilizes machine learning algorithms for classification and anomaly detection; and a response tier that automates alerts, containment, and system fortification. Through the integration of deception and adaptive analytics, the system attains elevated detection precision and robustness against advanced threats like polymorphic malware, sophisticated persistent threats, and insider assaults. Scalability considerations, integration of systems with SIEM and SOAR platforms, and management of ethical risks are also discussed. The chapter contends that these hybrid architectures signify a shift from reactive to proactive defense, with deception-based intelligence being systematically used to maintain the adaptability and enduring efficiency of IDS infrastructures.