A Hybrid System Using Genetic Algorithm for Anomaly Intrusion Detection

This paper proposes a new approach to design the system using a hybrid of misuse and anomaly detection for training of normal and attack packets respectively. The hybrid intrusion detection system combines the K-means and the genetic algorithm for anomaly detection. This algorithm operates on the KDD-99 Data set, this data set is used worldwide for evaluating the performance of different intrusion detection systems. The system can detect the intrusions and further classify them into four categories Denial of Service (DoS), U2R (User to Root), R2L (Remote to Local), and probe. The main goal is to reduce the false alarm rate of IDS.