Abstract
Intrusion detection systems are becoming increasingly important in network security, which have been widely used to protect our terminal devices against network attacks. However, classification of imbalanced datasets is one of the main challenges in intrusion detection. Traditional methods are biased to the majority classes, which cannot effectively detect the minority classes. This paper presents a novel ensemble classification technique for intrusion detection, which consists of dual evolution. The first evolution is run to select the features from the original training set, which are then used to extract the training set for the basic classifiers. After that, the second one is executed to choose a preferred combination from the base classifiers. When compared to other intrusion detection algorithms, the experimental results on the NSL-KDD and AWID datasets validate the advantages of the proposed algorithm.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 61876110, 61836005, and 61672358, the Joint Funds of the NNFC under Key Program Grant U1713212, and the Shenzhen Scientific Research and Development Funding Program under grant JCYJ20190808164211203.
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Lin, Q., Hu, C., Chen, S., Huang, P. (2020). An Ensemble Classification Technique for Intrusion Detection Based on Dual Evolution. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_53
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DOI: https://doi.org/10.1007/978-3-030-60802-6_53
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