A New Data Stream Clustering Approach about Intrusion Detection
Zhuo Qun Li · Advanced materials research · 2014
Intrusion detection is one of the most important techniques for protecting network security. In addition, intrusion detection model can be used to recognize real-time pattern, which has important practical significance for real-time intrusion detection. However, due to the sheer speed and scale of the data, data points must often be analyzed in real time. The one-pass-through requirement and the lack of efficient clustering algorithms to identify intrusion patterns limit the power and scalability of this approach. A data stream clustering algorithm is proposed for real-time network intrusion detection. By introducing the new hashing mechanism, the method can quickly find the clustering patterns in the data stream. The method significantly reduces the false alarm rate of intrusion detection, and improves the performance of intrusion detection system.