A Deception-Based Intrusion Prevention Framework for Proactive Network Security Using Behavioral Threat Analysis

Abstract

The rapid expansion of networked systems has led to an increase in sophisticated cyber threats that frequently bypass traditional security mechanisms. Conventional defenses largely rely on signature-based or rule-based techniques, which are limited in their ability to detect unknown or advanced attacks. To address these challenges, this paper proposes a Deceptive Intrusion Prevention System (DIPS) that transitions network security from a reactive model to a proactive, intelligence-driven approach. The proposed architecture employs strategically deployed decoy resources and deceptive information to divert attackers away from critical assets while monitoring their behavior within a controlled environment. The framework combines deception, behavioral analysis, and automated mitigation within a unified intrusion prevention architecture. By analyzing attacker interactions with deceptive components, the system accurately identifies malicious activity and enables real-time response actions such as isolation and blocking. Experimental evaluation conducted in a controlled network environment demonstrates that the proposed approach improves detection accuracy, reduces false positives, and enhances overall system resilience. The results further show that deception-based intrusion prevention effectively delays attackers and generates actionable threat intelligence, strengthening proactive network defense.