Intrusion detection system for cloud environment based on convolutional neural networks and PSO algorithm

Abstract

Authentication of clients and their applications to cloud services is a major concern. Network security and the identification of hostile activities are greatly aided by intrusion detection systems (IDS). In general, optimisation strategies can be applied to improve IDS model performance. Convolutional neural networks (CNN) and other deep learning (DL) algorithms is utilised to enhance IDS’s capability to identify and categories intrusions. IDSs can identify prior attacks, adapt to changing threats, and minimise false positives by utilising these strategies. In this work, a lightweight CNN is proposed for intrusion detection in cloud environment. The main contribution of this research is to use particle swarm optimization (PSO), ametaheuristic algorithm to find the CNNs optimal parameters that comprise the number of convolutional layers, the size of the filter utilized in the convolutional procedure, the number of convolutional filters, and the batch size. Heuristic based searches are useful for solving these kinds of problems. The experimental outcomes demonstrate that the proposed method reaches 91.70% of accuracy, 91.82% of precision, 91.99% of recall and 91.90% of F1-score. Cloud providers can gain from improved security measures by incorporating the proposed IDS paradigm into cloud settings, thereby minimizing unauthorized access and any data breaches.