S. Sureshkumar

Work place: Karpagam Academy of Higher Education, Department of Computer Science and Engineering, Coimbatore, 641 021, India

E-mail: sureshkumarcse2022@gmail.com


Research Interests:


Mr. S. Sureshkumar is a Research Scholar in Department of CSE at Karpagam Academy of Higher Education and Research, Coimbatore. Tamilnadu. He has completed his Undergraduate and Post graduate studies in Computer Science and Engineering in Anna University, Chennai. He has 14 years of academic experience in teaching. He has published 8 books and over 20 publications in highly cited Journals and Conferences. He is a professional body member of ISTE and IAENG.

Author Articles
Detection of DDOS Attacks on Cloud Computing Environment Using Altered Convolutional Deep Belief Networks

By S. Sureshkumar G. K. D. Prasanna Venkatesan R. Santhosh

DOI: https://doi.org/10.5815/ijcnis.2023.05.06, Pub. Date: 8 Oct. 2023

The primary benefits of Clouds are that they can elastically scale to meet variable demands and provide corresponding environments for computing. Cloud infrastructures require highest levels of protections from DDoS (Distributed Denial-of-Services). Attacks from DDoSs need to be handled as they jeopardize availability of networks. These attacks are becoming very complex and are evolving at rapid rates making it complex to counter them. Hence, this paper proposes GKDPCAs (Gaussian kernel density peak clustering techniques) and ACDBNs (Altered Convolution Deep Belief Networks) to handle these attacks. DPCAs (density peak clustering algorithms) are used to partition training sets into numerous subgroups with comparable characteristics, which help in minimizing the size of training sets and imbalances in samples. Subset of ACDBNs get trained in each subgroup where FSs (feature selections) of this work are executed using SFOs (Sun-flower Optimizations) which evaluate the integrity of reduced feature subsets. The proposed framework has superior results in its experimental findings while working with NSL-KDD and CICIDS2017 datasets. The resulting overall accuracies, recalls, precisions, and F1-scoresare better than other known classification algorithms. The framework also outperforms other IDTs (intrusion detection techniques) in terms of accuracies, detection rates, and false positive rates.

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