Performance of Interval-Based Features in Anomaly Detection by Using Machine Learning Approach
Kriangkrai Limthong · International Journal of Machine Learning and Computing · 2014
Detecting various anomalies or unusual incidents in computer network traffic is one of the great challenges for both researchers and network administrators.If they had an efficient method that could detect network traffic anomalies quickly and accurately, they would be able to prevent security problems or network congestion caused by such anomalies.Therefore, we conducted a series of experiments to examine which and how interval-based network traffic features affect anomaly detection by using three famous machine learning algorithms: the naï ve Bayes, k-nearest neighbor, and support vector machine.Our findings would help researchers and network administrators to select effective interval-based features for each particular type of anomaly, and to choose a proper machine learning algorithm for their own network system.