An Efficient Support Vector Machine Approach for Network Intrusion Detection and Classification
Keywords:
Support Vector Machine, Network Intrusion Detection, Machine Learning, Cybersecurity, Classification, Network Security, Feature Selection, Kernel Optimization, Intrusion Detection System, Anomaly DetectionAbstract
The rapid growth of computer networks and internet-based applications has increased the complexity of cybersecurity threats, making network intrusion detection an essential component of information security. Traditional intrusion detection systems often encounter difficulties in identifying previously unknown attacks, managing high-dimensional network traffic data, and reducing false-positive detection rates. This study examines an efficient Support Vector Machine (SVM) approach for network intrusion detection and classification. SVM is a supervised machine learning technique that provides effective classification by identifying optimal hyperplanes that distinguish normal network activities from malicious traffic. The proposed approach incorporates network traffic preprocessing, feature selection, model training, kernel optimization, and performance evaluation to improve intrusion detection accuracy and computational efficiency. The methodology considers the classification of network activities into normal and different categories of attacks, including denial-of-service, probe, user-to-root, and remote-to-local attacks. Performance is assessed using accuracy, precision, recall, F1-score, and false-positive rate. The discussion emphasizes the advantages of SVM in handling high-dimensional datasets, improving classification reliability, and supporting effective network security monitoring. The study also identifies challenges associated with large-scale traffic processing, class imbalance, and evolving cyber threats. The findings provide a theoretical and methodological foundation for developing efficient machine learning-based intrusion detection systems capable of strengthening network security
References
1. Axelsson, S. (2000). The base-rate fallacy and the difficulty of intrusion detection. ACM Transactions on Information and System Security, 3(3), 186–205. https://doi.org/10.1145/357830.357849
2. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121–167. https://doi.org/10.1023/A:1009715923555
3. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297. https://doi.org/10.1007/BF00994018
4. Denning, D. E. (1990). A new paradigm for intrusion detection and explanation. In Proceedings of the 1990 IEEE Computer Society Symposium on Research in Security and Privacy (pp. 199–202). IEEE.
5. Kim, D. S., & Park, J. S. (2003). Network-based intrusion detection with support vector machines. In Information Networking: Networking Technologies for Broadband and Mobile Networks (pp. 747–756). Springer.
6. Khan, L., Awad, M., & Thuraisingham, B. (2007). A new intrusion detection system using support vector machines and hierarchical clustering. The VLDB Journal, 16, 507–521. https://doi.org/10.1007/s00778-006-0002-5
7. Lee, W., & Stolfo, S. J. (1998). Data mining approaches for intrusion detection. In Proceedings of the 7th USENIX Security Symposium (pp. 79–93).
8. Lee, W., & Stolfo, S. J. (2000). A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security, 3(4), 227–261. https://doi.org/10.1145/382912.382914
9. Mukkamala, S., Janoski, G., & Sung, A. H. (2002). Intrusion detection using neural networks and support vector machines. In Proceedings of the International Joint Conference on Neural Networks (Vol. 2, pp. 1702–1707). IEEE. https://doi.org/10.1109/IJCNN.2002.1007774
10. Mukkamala, S., & Sung, A. H. (2003). Identifying important features for intrusion detection using support vector machines and neural networks. In Proceedings of the 2003 Symposium on Applications and the Internet (pp. 209–216). IEEE. https://doi.org/10.1109/SAINT.2003.1183050
11. Osuna, E., Freund, R., & Girosi, F. (1997). An improved training algorithm for support vector machines. In Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (pp. 276–285). IEEE.
12. Platt, J. C. (1999). Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, & A. J. Smola (Eds.), Advances in kernel methods: Support vector learning (pp. 185–208). MIT Press.
13. Vapnik, V. N. (1995). The nature of statistical learning theory. Springer.
14. Vapnik, V. N. (1998). Statistical learning theory. Wiley.
15. Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (pp. 103–114). ACM. https://doi.org/10.1145/233269.233324
