A machine learning framework for network anomaly detection using SVM and GA

In today's world of computer security, internet attacks such as Dos/DDos, worms, and spyware continue to evolve as detection techniques improve. It is not easy, however, to distinguish such new attacks using only knowledge of pre-existing attacks. In this paper we concentrate on machine learning techniques for detecting attacks from internet anomalies. Our machine learning framework consists of two major components: Genetic Algorithm (GA) for feature selection and Support Vector Machine (SVM) for packet classification. By experiment we also demonstrate that our proposed framework out performs currently employed real-world NIDS.