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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...
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Context 1
... efficiency and generalization performance make ELM an attractive machine learning tool, particularly suitable for large-scale datasets and real-time applications that require fast training. The architecture of the Extreme Learning Machine (ELM) is shown in Figure 1. ...
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... sample number is set to 5000. The results are presented in Figure 10. The experimental findings denote that, in both scenarios of with and without feature selection, detection latency incrementally elevates in tandem with an increase in the number of ELM hidden nodes. ...
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... enhancement and mitigation are due to the reduction in computational load during Finally, we compared the Extreme Learning Machine optimized with quantum particle swarm optimization (Q-ELM) to the original Extreme Learning Machine (ELM) algorithm in terms of test accuracy when the number of training samples was 5000. The results shown in Figure 11 indicate that under the same training sample conditions, our proposed algorithm, optimized with quantum particle swarm optimization, can significantly improve training accuracy. ...
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