Abstract
Recent research in networking attacks shows that the distributed denial-of-service (DDoS) attack is increase by 19% in last year. The DDoS attack is performed by flooding packets from various distributed bots nodes to victim server from attacker. Detection of distributed denial-of-service attack is challenging tasks due to availability of high-computational resources and technology. This paper proposes the multiple clustering methods for DDoS attack detection. The hierarchical and K-means with PCA clustering convert the unlabeled data traffic into labeled data traffic. The K-nearest neighbors, SVM, logistic regression, and random forest classification are used to classify labeled data traffic into normal and DDoS attack traffics. The proposed method is validated using KDD CUP dataset. The proposed method gives high accuracy and reduces false rate as compared to existing methods.
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References
S. Miller, C. Busby-Earle, ‘The role of machine learning in botnet detection, in 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 359–364 (2016)
M. Aamir, S.M.A. Zaidi, Clustering based Semi-supervised machine learning for DDoS attack classification. J. King Saud Univ. Comput. Inf. Sci. (2019)
S. Fitriani, S. Mandala, M.A. Murti, Review of semi-supervised method for intrusion detection system, in Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast), pp. 36–41 (2016)
Y. Gu, Y. Wang, Z. Yang, F. Xiong, Y. Gao, Multiple-features-based semisupervised clustering DDoS detection method. Math. Problems Eng. (2017)
R. Zhong, G. Yue, DDoS detection system based on data mining, in Proceedings of 2nd International Symposium on Networking and Network Security, Jinggang Shan, China, pp. 062–065, 2–4 Apr 2010
M. Jonker, A. King, J. Krupp, C. Rossow, A. Sperotto, A. Dainotti, Millions of targets under attack: a macroscopic characterization of the DoS ecosystem, in Proceedings of the 2017 Internet Measurement Conference, pp. 100–113 (2017)
J. Wu, X. Wang, X. Lee, B. Yan, Detecting DDoS attack towards DNS server using a neural network classifier, in International Conference on Artificial Neural Networks, pp. 118–123 (Springer, Berlin, 2010)
J. Kim, A. Sim, B. Tierney, S. Suh, I. Kim, Multivariate network traffic analysis using clustered patterns. Computing, pp. 1–23 (2018)
X. Zhu, Semi-supervised Learning Literature Survey. Computer Science, University Wisconsin-Madison 2(3) (2006)
G. Xiang, W. Min, ‘Applying Semi-supervised cluster algorithm for anomaly detection, in 2010 Third International Symposium on Information Processing (ISIP), pp. 43–45 (2010)
H. Beitollahi, G. Deconinck, Analyzing well-known countermeasures against distributed denial of service attacks. Comput. Commun. 35(11), 1312–1332 (2012)
M. Wang, Y. Lu, J. Qin, A dynamic MLP-based DDoS attack detection method using feature selection and feedback. Comput. Secur. 88, 101645 (2020)
S. Soheily-Khah, P.-F. Marteau, N. Béchet, Intrusion detection in network systems through hybrid supervised and unsupervised machine learning process: a case study on the ISCX dataset, in 2018 1st International Conference on Data Intelligence and Security (ICDIS), pp. 219–226 (2018)
M. Idhammad, K. Afdel, M. Belouch, Semi-supervised machine learning approach for DDoS detection. Appl. Intell. pp. 1–1 (2018)
K. Kato, V. Klyuev, Development of a network intrusion detection system using Apache Hadoop and Spark, in IEEE Conference on Dependable and Secure Computing, pp. 416–423 (2017)
A.S. Boroujerdi, S. Ayat, A robust ensemble of neuro-fuzzy classifiers for DDoS attack detection, in 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT), pp. 484–487 (2013)
J. François, I. Aib, R. Boutaba, FireCol: a collaborative protection network for the detection of flooding DDoS attacks. IEEE/ACM Trans. Netw. (TON) 20, 1828–1841 (2012)
B. Jia, Y. Ma, X. Huang, Z. Lin, Y. Sun, A novel real time DDoS attack detection mechanishm based on MDRA algorithm in big data. Hindawi Publishing Corporation Math. Probl. Eng. Vol. 2016
A. Scherrer, N. Larrieu, P. Owezarski, P. Borgnat, P. Abry, Non-Gaussian and long memory statistical characterizations for Internet traffic with anomalies. IEEE Trans. Dependable Secure Comput. 4(1), 56–70 (2007)
Z.Y. Tan, A. Jamdagni, X.J. He, P. Nanda, R.P. Liu, A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE Trans. Parallel Distrib. Syst. 25(2), 447–456 (2014)
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Talmale, G., Shrawankar, U. (2021). Multiple Clustering Method for Real-Time Distributed Denial-of-Service Attack Detection. In: Singh Mer, K.K., Semwal, V.B., Bijalwan, V., Crespo, R.G. (eds) Proceedings of Integrated Intelligence Enable Networks and Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6307-6_58
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DOI: https://doi.org/10.1007/978-981-33-6307-6_58
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