Detecting Denial of Service attacks using machine learning algorithms

Abstract Currently, Distributed Denial of Service Attacks are the most dangerous cyber danger. By inhibiting server's ability to provide resources genuine customers, affected resources, such as bandwidth and buffer size, slowed down. A mathematical model for distributed denial-of-service attacks is proposed in this study. Machine learning algorithms Logistic Regression Naive Bayes, used detect normal scenarios. The CAIDA 2007 Dataset experimental machine trained tested using dataset validated. Weka data mining platform study implementation results same analysed compared. Other with respect denial service compared existing work.