Parallel Optimization Technique to Improve the Performance of Lightweight Intrusion Detection Systems

In recent years, the need for effective and lightweight intrusion detection systems (LIDS) has grown significantly due to the widespread adoption of Internet of Things (IoT) devices and the increasing number of cyber threats. This paper presents a novel parallel optimization technique to enhance the performance of LIDS in terms of accuracy, detection rate, and computational efficiency. Our approach employs a combination of machine learning algorithms and parallel computing to process and analyze network data in a highly efficient manner. We investigate the effectiveness of various feature selection techniques and ensemble models in the context of parallel processing to optimize the overall performance of the LIDS. Furthermore, we propose a hybrid model that seamlessly integrates the selected feature subsets and ensemble classifiers for improved accuracy and reduced false alarm rates. To evaluate the proposed technique, we conduct extensive experiments using real-world datasets and compare our approach with existing state-of-the-art LIDS. The results demonstrate that our parallel optimization technique significantly outperforms the current methods, achieving higher detection rates, better accuracy, and reduced computational overhead. This research contributes to the development of more effective and resource-efficient LIDS, which are crucial for the security of IoT ecosystems and other resource-constrained environments.