Cuckoo Search Algorithm Based Intrusion Detection System of Cybersecurity
Shilpa Jain, Sandeep Kumar Sunori, Amit Mittal, Pradeep Kumar Juneja · 2025
This research article investigates the implementation of Cuckoo Search (CS) optimization technique to enhance intrusion detection systems (IDS) of cybersecurity using MA TLAB. Detection of intrusion plays a crucial role in finding and mitigating unpermitted access attempts in networks. The efficacy of an IDS is defined majorly by two metrics: detection accuracy, which means network's ability to identify correct threats, and other one is false positive rate, which includes cases of mistakenly identified as benign activities. In this research work, attempt has been made to optimize two key parameters of IDS viz. decision threshold and model weight, to improve the performance. The CS algorithm, a heuristic and bio-inspired optimization method, is applied as it is capable of exploring complex search space efficiently. By employing probabilistic abandonment and levy flights mechanism, it bypasses local optima, hence exhibits improved global search efficacy. Further the impact of adjusting the abandonment probability (Pa) on the performance of algorithm, monitoring the progression of fitness values over several iterations, has been investigated. The fitness function of IDS is articulated to deliver maximum detection accuracy at the same time minimization of false positive rate, crafting a sturdy intrusion detection mechanism. Experimental outcomes exhibit the adaptation of CS algorithm in optimization of IDS attributes, making it a robust approach for improving cybersecurity systems' detecting capabilities in real-time applications.