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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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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