Work place: Department of CSE, Acharya Nagarjuna University, Guntur, AP, India
Research Interests: Natural Language Processing
Kishore Babu Dasari born in Vaivaka, on 26th April, 1983, Pursuing Ph.D in Acharya Nagarjuna university, Andhrapradesh, India in Computer Science and Engineering. Studied M.Tech (Software Engineering) in Avanthi Institute of Technology & Science and B. Tech (CSE) in Gudlavalleru Engineering College.
He is working at the Keshav Memorial Institute of Technology as an Assistant Professor in the CSE Department. He has total 13 years of teaching experience.
He is the member of the CSI professional Society. He qualified UGC NET. He awarded NPTEL translator for translating one course into Indian regional language Telegu.
DOI: https://doi.org/10.5815/ijcnis.2022.06.07, Pub. Date: 8 Dec. 2022
The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial of Service (DDoS) is a type of cyber attack that prevents legitimate users from accessing network system resources. To minimize major damage, quick and accurate DDoS attack detection techniques are essential. To classify target classes, machine learning classification algorithms are faster and more accurate than traditional classification methods. This is a quantitative research applies Logistic Regression, Decision Tree, Random Forest, Ada Boost, Gradient Boost, KNN, and Naive Bayes classification algorithms to detect DDoS attacks on the CIC-DDoS2019 data set, which contains eleven different DDoS attacks each containing 87 features. In addition, evaluated classifiers’ performances in terms of evaluation metrics. Experimental results show that AdaBoost and Gradient Boost algorithms give the best classification results, Logistic Regression, KNN, and Naive Bayes give good classification results, Decision Tree and Random Forest produce poor classification results.[...] Read more.
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