Classification based on distribution of average matching degree and Gaussian function and its application to intrusion detection
Yuhong Li, Shingo Mabu, Nannan Lu, Kotaro Hirasawa · Society of Instrument and Control Engineers of Japan · 2012
With the rapid development of the Internet, Internet security is becoming an important problem recently. Therefore, many techniques for intrusion detection have been proposed to protect networks effectively. In this paper, a new classification model, named classification with average matching degree and gaussian function, is proposed and combined with the class association rule mining of Genetic Network Programming (GNP). The proposed classification algorithm can efficiently classify a new access data into a class of normal, misuse or anomaly. The simulations are based on NSL-KDD data set.