Research Article
A Performance Analysis Approach for Network Intrusion Detection Algorithms
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@INPROCEEDINGS{10.1007/978-3-030-72792-5_20, author={Zhihao Wang and Dingde Jiang and Yuqing Wang and Junyang Zhang}, title={A Performance Analysis Approach for Network Intrusion Detection Algorithms}, proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part I}, proceedings_a={SIMUTOOLS}, year={2021}, month={4}, keywords={Network intrusion detection Machine learning Random forest Multilayer Perceptron Performance analysis}, doi={10.1007/978-3-030-72792-5_20} }- Zhihao Wang
Dingde Jiang
Yuqing Wang
Junyang Zhang
Year: 2021
A Performance Analysis Approach for Network Intrusion Detection Algorithms
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-72792-5_20
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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