Enhancing network security with information-guided-enhanced Runge Kutta feature selection for intrusion detection

Intrusion detection system (IDS) classify network traffic as either threatening or normal based on data features, aiming to identify malicious activities attempting to compromise computer systems. However, the volume of intrusion-related data is increasing daily, and the redundant features within this data hinder the improvement of IDS classification performance and efficiency. This study introduces a wrapper feature selection model, denoted as bICSRUN-KNN, with Runge–kutta optimization for information-guided communication (ICSRUN) to detect system intrusions. Comparative experiments on the IEEE CEC 2014 benchmark functions demonstrate ICSRUN’s superiority over other algorithms. Subsequently, comparative experiments are conducted using 12 UCI datasets, NSL-KDD, ISCX-URL-2016, ISCX-Tor-NonTor-2017, and LUFlow Network, against competing algorithms. Experimental results demonstrate that the bICSRUN-KNN model achieved remarkable accuracy rates of 98.705% and 98.341% in the binary and multiclass contexts of NSL-KDD. For ISCX-URL-2016, ISCX-Tor-NonTor-2017, and LUFlow Network, accuracy rates of 96.107%, 99.772%, and 88.748% are respectively attained.