Modified Whale Algorithm and Morley PSO-ML-Based Hyperparameter Optimization for Intrusion Detection

Intrusion detection averts a network from probable intrusions by inspecting network traffic to ensure its integrity, availability, and confidentiality. Though IDS seems to eliminate malicious traffic, intruders have endeavored to use different approaches for undertaking attacks. Hence, effective intrusion detection is vital to detect attacks. Concurrently, the evolvement of machine learning (ML), attacks could be identified by evaluating the patterns and learning from them. Considering this, conventional works have attempted to perform intrusion detection. Nevertheless, they lacked about high false alarm rate (FAR) and low accuracy rate due to inefficient feature selection. To resolve these existing pitfalls, this research proposed a modified whale algorithm (MWA) based on nonlinear information gain to select significant and relevant features. This algorithm assures huge initialization to improve local search ability as the agent’s positions are usually near the optimal solution. It is also utilized for an adaptive search for an optimal combination of features. Following this, the research proposes Morlet particle swarm optimization hyperparameter optimization (MPSO-HO) to improve the convergence rate of the algorithm by consenting it to produce from the local optimization by improving its capability. Standard metrics assess the proposed system to confirm the optimal performance of the proposed system. Outcomes explore the effective ability of the proposed system in intrusion detection.