The protection of networks from various cyber threats depends heavily on the “Intrusion Detection System” (IDS) due to continually increasing network complexity. Passive IDS observes network traffic for detection purposes while avoiding system changes making it an appropriate tool for vulnerability finding without system disruption. Multiple issues surface in existing systems because they need to balance detection accuracy with computing speed. The research presents an improved version of “Self-Adaptive Differential Evolution” framework (SADE) that optimizes Passive IDS functionality. Advances in optimization allow the system to modify its operation according to altering network conditions and security threats thus helping it identify threats effectively with limited false alarm occurrences. The proposed model optimizes its three core capabilities including feature selection together with parameter adjustment and real-time adaptation to boost its performance for intrusion detection. The solution provides both security boost and performance scalability for passive intrusion detection while increasing system effectiveness.