Quantum particle Swarm optimized extreme learning machine for intrusion detection
Document Type
Article
Publication Date
7-1-2024
Abstract
Ensuring a secure online environment hinges on the timely detection of network attacks. Nevertheless, existing detection methods often grapple with the delicate balance between speed and accuracy. In this paper, we introduce a novel intrusion detection algorithm that marries quantum particle swarm optimization with an extreme learning machine (QPSO-ELM). Firstly, we present a feature selection algorithm grounded in partitioned gains to distill vital features from data samples, thereby diminishing feature count to amplify both model training speed and accuracy. Subsequently, we unveil an intrusion detection scheme underpinned by QPSO-ELM, capable of achieving exceptional levels of training and detection speed, all while maintaining high accuracy. Finally, we fine-tune the trained model using the proposed hidden layer node selection algorithm, reducing the detection model size without compromising detection accuracy, thus further elevating its speed. The experiment results indicate that compared to the current baseline, our proposed intrusion detection scheme achieves the best results in terms of accuracy, precision, recall, and detection latency. Furthermore, the ablation experiment results demonstrate the effectiveness of our proposed method in improving both detection speed and detection accuracy.
Keywords
Network security, Feature selection, Quantum particle swarm optimization, Extreme learning machine, Intrusion detection
Divisions
fsktm
Funders
Liaoning Provincial Department of Education Research (LJKZ0208),Scientific Research Foundation for Advanced Talents from Shenyang Aerospace University (18YB06)
Publication Title
Journal of Supercomputing
Volume
80
Issue
10
Publisher
Springer
Publisher Location
VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS