Abstract
With the development of mobile Internet and cloud computing, the amount of network traffic has been significantly increased. Security problems have drawn a lot of attention, while traditional methods are becoming increasingly unsuitable for it. In this paper, three machine learning algorithms are employed to detect network intrusion, including KNN, Random Forest, and Multilayer Perceptron. Performance evaluation and comparison between them are conducted, in terms of precision, recall, training time, etc. Simulation results on the NSL-KDD, a benchmark data set of network intrusion detection, show that the Random Forest algorithm exhibits higher detection accuracy and remarkably shorter training time.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (No. 61571104), the Sichuan Science and Technology Program (No. 2018JY0539), the Key projects of the Sichuan Provincial Education Department (No. 18ZA0219), the Fundamental Research Funds for the Central Universities (No. ZYGX2017KYQD170), the CERNET Innovation Project (No. NGII20190111), the Fund Project (Nos. 61403110405, 315075802), and the Innovation Funding (No. 2018510007000134). The authors wish to thank the reviewers for their helpful comments.
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Wang, Z., Jiang, D., Wang, Y., Zhang, J. (2021). A Performance Analysis Approach for Network Intrusion Detection Algorithms. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_20
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DOI: https://doi.org/10.1007/978-3-030-72792-5_20
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