Abstract
Due to the rapid use of the internet, the Distributed Denial of Service (DDoS) attack is affected by E-Commerce, government, and private IT infrastructure. Intrusion Detection System is the best way to deal with the detection of DDoS attacks. In this paper, we focused on the feature selection process to improve the performance by the selection of important features. Information Gain with Ranker algorithm is used for the feature selection process. After the feature selection process, the proposed system uses Random Forest, J48, LMT (Logistic Model Tree) classifiers for the detection of the DDoS attack. The proposed system is tested with the help of CICIDS2017 dataset. The experimentation result shows that J48 classifier provides improved detection rate as compared to Random Forest and LMT with important features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
DDoS Attack Report Q2 2018, by haltDoS blog
Pandey, V.C., Peddoju, S.K., Deshpande, P.S.: Sadhana 43, 32 (2018). https://doi.org/10.1007/s12046-018-0800-7
Deshpande, P., Sharma, S.C., Sateesh Kumar, P.: Security threats in cloud computing. In: International Conference on Computing, Communication and Automation (ICCCA), Nida-India, pp. 632–636 (2015)
Lei, S.: A feature selection method based on information gain and genetic algorithm. In: 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), vol. 2, pp. 355–358. IEEE (2012)
Alhaj, T.A., Siraj, M.M., Zainal, A., Elshoush, H.T., Elhaj, F.: Feature selection using information gain for improved structural-based alert correlation. PLoS One 11(11), e0166017 (2016)
Shree Singh, N.A., Singh, K.J., De, T.: Distributed denial of service attack detection using naive Bayes classifier through info gain feature selection. In: Proceedings of the International Conference on Informatics and Analytics, p. 54. ACM (2016)
Tao, P., Sun, Z., Sun, Z.: An improved intrusion detection algorithm based on GA and SVM. IEEE Access 6, 13624–13631 (2018)
Deshpande, P., Aggarwal, A., Sharma, S.C., Kumar, S.P.: Distributed port-scan attack in cloud environment. In: Fifth International Conference on Computational Aspects of Social Networks (CASoN), pp. 27–31 (2013)
Shakya V., Makwana, R.R.S.: Feature selection based intrusion detection system using the combination of DBSCAN, K-Mean++ and SMO algorithms. In: 2017 International Conference on Trends in Electronics and Informatics (ICEI), pp. 928–932. IEEE (2017)
Alkasassbeh, M., Al-Naymat, G., Hamadneh, N., Obeidat, I., Almseidin, M.: Intensive preprocessing of KDD Cup 99 for network intrusion classification using machine learning techniques. arXiv preprint arXiv:1805.10458 (2018)
Aljawarneh, S., Yassein, M.B., Aljundi, M.: An enhanced J48 classification algorithm for the anomaly intrusion detection systems. Cluster Comput. 1–17 (2017)
Alazab, A., Hobbs, M., Abawajy, J., Alazab, M.: Using feature selection for intrusion detection system. In: 2012 International Symposium on Communications and Information Technologies (ISCIT), pp. 296–301. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patil, A., Kshirsagar, D. (2020). Towards Feature Selection for Detection of DDoS Attack. In: Iyer, B., Deshpande, P., Sharma, S., Shiurkar, U. (eds) Computing in Engineering and Technology. Advances in Intelligent Systems and Computing, vol 1025. Springer, Singapore. https://doi.org/10.1007/978-981-32-9515-5_21
Download citation
DOI: https://doi.org/10.1007/978-981-32-9515-5_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9514-8
Online ISBN: 978-981-32-9515-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)