Work place: Karpagam Academy of Higher Education, Department of Computer Science and Engineering, Coimbatore, 641021, India
Research Interests: Artificial Intelligence, Distributed Computing, Cloud Computing
Dr. R. Santhosh received his B.Tech degree in Information Technology from K.S.R College of Technology in 2006, M.E degree in Software Engineering from Sri Ramakrishna Engineering College in 2009, M.B.A in Education Management from Alagappa University in 2011 and Ph.D in Computer Science and Engineering from Karpagam Academy of Higher Education in 2016. He is currently working as a Professor and Head in the Department of Computer Science and Engineering, Faculty of Engineering at Karpagam Academy of Higher Education. He has published more than 84 Research Articles in International Journals indexed in Scopus and Web of Science. He has published 2 International Patents. His current research interests include Cloud Computing, Distributed & Parallel Computing, Artificial Intelligence and Data Science.
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.[...] Read more.
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