Here is a great 2007 MS Thesis from Liwei (Vivian) Kuang from School of Computing, Queen’s University, Kingston, Ontario, Canada. DNIDS: A Dependable Network Intrusion Detection System Using the CSI-KNN Algorithm
I’m happy she quoted my Cosine Similarity Tutorial.
Part of the abstract states: “In this thesis, we propose a Dependable Network Intrusion Detection System(DNIDS) based on the Combined Strangeness and Isolation measure K-Nearest Neighbor(CSI-KNN) algorithm. The DNIDS can effectively detect network intrusionswhile providing continued service even under attacks. The intrusion detection algorithmanalyzes different characteristics of network data by employing two measures:strangeness and isolation. Based on these measures, a correlation unit raises intrusionalerts with associated confidence estimates. In the DNIDS, multiple CSI-KNNclassifiers work in parallel to deal with different types of network traffic. An intrusiontolerantmechanism monitors the classifiers and the hosts on which the classifiers resideand enables the IDS to survive component failure due to intrusions. As soon asa failed IDS component is discovered, a copy of the component is installed to replaceit and the detection service continues.”
“We evaluate our detection approach over the KDD’99 benchmark dataset. Theexperimental results show that the performance of our approach is better than the bestresult of the KDD’99 contest winner. In addition, the intrusion alerts generated byour algorithm provide graded confidence that offers some insight into the reliabilityof the intrusion detection. To verify the survivability of the DNIDS, we test theprototype in simulated attack scenarios. In addition, we evaluate the performanceof the intrusion-tolerant mechanism and analyze the system reliability.”