A machine learning framework for network anomaly detection using SVM and GA
Taeshik Shon, Yongdae Kim, Cheolwon Lee, Jongsub Moon · 2005
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 the authors focused on machine learning techniques for detecting attacks from Internet anomalies. The machine learning framework consists of two major components: genetic algorithm (GA) for feature selection and support vector machine (SVM) for packet classification. By experiment it is also demonstrated that the proposed framework outperforms currently employed real-world NIDS.