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Enhancing network security through machine learning: A study on intrusion detection system using supervised algorithms
- 1 Virginia Polytechnic Institute and State University
- 2 Campbellsville University
- 3 Hebei Zhengzhong high school
* Author to whom correspondence should be addressed.
Abstract
The topic of Intrusion Detection System (IDS) has become a highly debated issue in cybersecurity, generating intense discussions among experts in the field. IDS can be broadly categorized into two types: signature-based and anomaly-based. Signature-based IDS employ a collection of known network attacks to identify the precise attack the network is experiencing, while anomaly-based IDS employ machine learning models to detect anomalies present in the network traffic that could indicate a potential attack. In this study, we concentrate on anomaly-based IDS, evaluating the effectiveness of three supervised learning algorithms - Decision Tree (DT), Naive Bayes (NB), and K-Nearest Neighbor (KNN) - to determine the most suitable algorithm for each dataset based on its source. We conducted tests to evaluate each algorithm's performance and choose the best one for each dataset. Our findings show that anomaly-based IDS is highly effective in enhancing network security, providing valuable insights for organizations looking to improve their security measures.
Keywords
Network Intrusion Detection, Decision Tree, Naive Bayes, K-Nearest Neighbor
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Cite this article
Huang,Z.;Li,Z.;Zhang,J. (2023). Enhancing network security through machine learning: A study on intrusion detection system using supervised algorithms. Applied and Computational Engineering,19,50-66.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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