KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Machine Intelligence & kNowledge Engineering Lab
MetadataShow full item record
AbstractHigh speed of processing massive audit data is crucial for an anomaly Intrusion Detection System (IDS) to achieve real-time performance during the detection. Abstracting audit data is a potential solution to improve the efficiency of data processing. In this work, we propose two strategies of data abstraction in order to build a lightweight detection model. The first strategy is exemplar extraction and the second is attribute abstraction. Two clustering algorithms, Affinity Propagation (AP) as well as traditional k-means, are employed to extract the exemplars, and Principal Component Analysis (PCA) is employed to abstract important attributes (a.k.a. features) from the audit data. Real HTTP traffic data collected in our institute as well as KDD 1999 data are used to validate the two strategies of data abstraction. The extensive test results show that the process of exemplar extraction significantly improves the detection efficiency and has a better detection performance than PCA in data abstraction. © 2010 Springer-Verlag.
PublisherSpringer Science + Business Media
Conference/Event name6th International Conference on Information Systems Security, ICISS 2010