Abstract
Benchmark datasets are available to test and evaluate intrusion detection systems. The benchmark datasets are characterized by high volume and dimensionality curse. The feature reduction plays an important role in a machine learning-based intrusion detection system to identify relevant and irrelevant features with respect to the classification. This paper proposes a method for the identification of reduced features for the classification of Denial of Service (DoS) attack. The reduced feature technique is based on Information Gain (IG) and Threshold Limit Value (TLV). The proposed approach detects DoS attack using a reduced feature set from the original feature set with PART classifier. The proposed approach is implemented and tested on CICIDS 2017 dataset. The experimentation shows improved results in terms of performance with a reduced feature set. Finally, the performance of the proposed system is compared with the original feature set.
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References
Shuster, E., LaSeur, L., Katz, O., Ragan, S.: Financial Services Attack Economy. Akamai Technologies (2019)
Salih, A.A., Abdulrazaq, M.B.: Combining best features selection using three classifiers in intrusion detection system. In: 2019 International Conference on Advanced Science and Engineering, pp. 94–99. IEEE (2019)
Shrivastava, R.K., Bashir, B., Hota, C.: Attack detection and forensics using honeypot in IoT environment. In: Fahrnberger, G., Gopinathan, S., Parida, L. (eds.) ICDCIT 2019. LNCS, vol. 11319, pp. 402–409. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05366-6_33
Dongre, S., Chawla, M.: Analysis of feature selection techniques for denial of service (DoS) attacks. In: 2018 4th International Conference on Recent Advances in Information Technology, pp. 1–4. IEEE (2018)
Salo, F., Ali, B., Aleksander, E.: Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Comput. Netw. 148, 164–175 (2019)
Xiao, Y., Xing, C., Zhang, T., Zhao, Z.: An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access 7, 42210–42219 (2019)
Wang, W., Du, X., Wang, N.: Building a cloud IDS Using an efficient feature selection method and SVM. IEEE Access 7, 1345–1354 (2018)
Selvakumar, B., Muneeswaran, K.: Firefly algorithm based feature selection for network intrusion detection. Comput. Secur. 81, 148–155 (2019)
David, J., Ciza, T.: Efficient DDoS flood attack detection using dynamic thresholding on flow-based network traffic. Comput. Secur. 82, 284–295 (2019)
Faizal, M., Zaki, M.M., Shahrin, S., Robiah, Y., Rahayu, S.S., Nazrulazhar, B.: Threshold verification technique for network intrusion detection system (2009). arXiv preprint: arXiv:0906.3843
Manzoor, I., Neeraj, K.: A feature reduced intrusion detection system using ANN classifier. Expert Syst. Appl. 88, 249–257 (2017)
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Kshirsagar, D., Kumar, S. (2020). Identifying Reduced Features Based on IG-Threshold for DoS Attack Detection Using PART. In: Hung, D., D´Souza, M. (eds) Distributed Computing and Internet Technology. ICDCIT 2020. Lecture Notes in Computer Science(), vol 11969. Springer, Cham. https://doi.org/10.1007/978-3-030-36987-3_27
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DOI: https://doi.org/10.1007/978-3-030-36987-3_27
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