G. K. D. Prasanna Venkatesan

Work place: Karpagam Academy of Higher Education, Department of Computer Science and Engineering, Coimbatore, 641021, India

E-mail: dean.engineering@kahedu.edu.in


Research Interests: Sensor, Wireless Networks, Machine Learning, Cloud Computing, IoT


Dr. G. K. D. Prasanna Venkatesan, Professor in the Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India.He has completed his Ph.D in “Beyond 4G Networks” from Anna University, Chennai and obtained his M.Tech from SASTRA University, Thanjavur in 2002 and B.E (Electronics and Communication Engineering) from Madurai Kamaraj University, Madurai in 2000. His expertise in 5G Technologies, Machine Learning, Data analytics, Cloud Computing, IoT, Wireless sensor networks. He worked as a Specialist, in design and development at Tata Elxsi, Bangalore. He published more than 120 research articles in the international journals and Conferences. So far, he guided 12 doctorates. He serves as editorial board for various referred journals. He has chaired many IEEE conferences in India and abroad.

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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