Machine Learning Based Classification Model for Network Traffic Anomaly Detection

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K. Shyam Sunder Reddy
Vempati Krishna
M. Prabhakar
Punna Srilatha
K.Gurnadha Gupta
Ravula Arun Kumar


In current days, cloud environments are facing a huge challenge from the attackers in terms of various attacks thrown to the cloud service providers. In both industry and academics, the problem of detection and mitigation of DDoS attacks is now a challenging issue. Detecting Distributed Denial of Service (DDos) threats is mainly a classification problem that can be addressed using data mining, machine learning and deep learning techniques. DDoS attacks can occur in any of the seven-layer OSI model's network. Hence, detecting the DDoS attacks is an important task for cloud service providers to overcome dangerous attacks and loss incurred to stake holders and also the provider.

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How to Cite
Reddy, K. S. S. ., Krishna, V. ., Prabhakar, M. ., Srilatha, P. ., Gupta, K. ., & Kumar, R. A. . (2023). Machine Learning Based Classification Model for Network Traffic Anomaly Detection. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 563–576.


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