Using Machine Learning Algorithm Detecting Denial Of Service Attacks
Keywords:
DDOS attacks, Machine learning for security, Mathematical model for Bandwidth Depletion, Throughput analysis of attack and normal scenarioAbstract
Currently, Distributed Denial of Service Attacks are the most dangerous cyber danger. By inhibiting the
server’s ability to provide resources to genuine customers, the affected server’s resources, such as
bandwidth and buffer size, are slowed down. A mathematical model for distributed denial-of-service
attacks is proposed in this study. Machine learning algorithms such as Logistic Regression and Naive
Bayes, are used to detect attacks and normal scenarios. The CAIDA 2007 Dataset is used for
experimen- tal study. The machine learning algorithms are trained and tested using this dataset and the
trained algorithms are validated. Weka data mining platform are used in this study for implementation
and results of the same are analysed and compared. Other machine learning algorithms used with respect
to denial of service attacks are com- pared with the existing work
