Enhancing Intrusion Detection Systems Using Metaheuristic Algorithms

Abstract

In the current network security framework, Intrusion Detection Systems (IDSs) happen to be among the major players in ensuring that the network activity is being monitored round the clock for any intrusions which may occur. The rising degree of cyber threats’ intricacy enforces the constant development of IDS methodologies to maintain effectiveness in detecting and reversing the emergence of any extra risks. Therefore, to settle the matter featured by, this research studies try to incorporate the most powerful metaheuristic algorithms, Lion Optimization Algorithm (LOA) and Grey Wolf Optimizer (GWO) in particular, to develop better detection accuracy and efficiency. The core obstacle recognized in this article is the fact that many systems of IDS send out false alarms and their mechanisms of detection of the true anomalies need to be improved immensely. In a nutshell, the change would unveil a fresh way of using LOA and GWO using them to promote the enhancement of internet defences systems in real-time. These schemes can discover previously unknown weaknesses or stealthy attacks. The core of this undertaking would consist in the conception and implementing of a Hybrid Network Intrusion Detection System, which will be created by blending the Lion Optimization Feature Selection (LOFS) and GWO smelters, denoted as LOFSGWO. Critically, the main purpose is to incorporate the GWO as a tool in the operations to cut down the dangerous parameters favourable towards an intrusion mechanism in the framework of a Hybrid CNN-LSTM Deep Learning system. Model tests reveal over 99.26% accuracy of low negative samples into out of a box that are served as testing as well as NSL-KDD dataset, which are similar to the simulation of WUSTL-EOM 2020 system. The obtained outcomes verify the relevance and efficiency of the suggested strategy, which may be used in the resolution of the issues faced in a network security today.