Support vector classifiers and network intrusion detection

Within network security, there is the task of intrusion detection. Intrusion detection is a classification task that attempts to discern if a given request for network service is an intrusion attempt or a safe request. Since the creation of the 1999 KDD Cup network intrusion data set, several machine learning approaches to this task have been found to be successful. In this work we propose using the successful support vector machine (SVM) learning approach to classify network requests. We use computational experiments to explore two factors that influence SVM performance in this task and demonstrate two novel approaches to this task.