Abstract
In the modern world of interconnected systems, network security is gaining importance and attracting a lot of new research and study. Intrusion detection systems (IDSs) form an integral part of network security. To enhance the security of a network, machine learning algorithms can be applied to detect and prevent network attacks. Taking advantage of the robust NSL-KDD dataset, we have employed the supervised learning algorithm random forests to train a model to detect various networking attacks. To further increase the classification accuracy of our model, we have employed the use of famous data mining technique of feature selection. Smart feature selection using Gini importance has been employed to reduce the number of features. Experimental results have shown that our model not only runs faster but also performs with a higher accuracy.
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References
Gyanchandani, M., Rana, J. L., & Yadav, R. N. (2012). Taxonomy of anomaly based intrusion detection system: A review. International Journal of Scientific and Research Publications, 2(12), 1–13.
Biau, G. (2012). Analysis of a random forests model. Journal of Machine Learning Research, 13, 1063–1095.
Yin, C., Zhu, Y., Fei, J., & He, X. A deep learning approach for intrusion detection using recurrent neural networks. In IEEE Access (Vol. 5).
C.-H. Lee, Su, Y.-Y., Lin, Y.-C., & Lee, S.-J. (2017). Machine learning based network intrusion detection. In 2017 2nd IEEE International Conference on Computational Intelligence and Applications.
Ingre, B., & Yadav, A. Performance analysis of NSL-KDD dataset using ANN. In SPACES-2015, Department of ECE, K L University.
Wahba, Y., ElSalamouny, E., & ElTawee, G. (2015, May). Improving the performance of multi-class intrusion detection systems using feature reduction. IJCSI International Journal of Computer Science Issues, 12(3).
Chae, H., Jo, B., Choi, S.-H., & Park, T. Feature selection for intrusion detection using NSL-KDD. Recent Advances in Computer Science.
Tavallaee, M., et al. (2009). A detailed analysis of the KDD CUP 99 data set. In Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications 2009.
Breiman, L. (2004). Consistency for a simple model of random forests in Technical Report 670. Technical report, Department of Statistics, University of California, Berkeley, USA.
Breiman, L. (2001). Random forests. Journal of Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324.
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Negandhi, P., Trivedi, Y., Mangrulkar, R. (2019). Intrusion Detection System Using Random Forest on the NSL-KDD Dataset. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-6001-5_43
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DOI: https://doi.org/10.1007/978-981-13-6001-5_43
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