K-means+id3 and dependence tree methods for supervised anomaly detection

In this dissertation, we present two novel methods for supervised anomaly detection. The first method "K-Means+ID3" performs supervised anomaly detection by partitioning the training data instances into k clusters using Euclidean distance similarity. Then, on each cluster representing a density region of normal or anomaly instances, an ID3 decision tree is built. The ID3 decision tree on each cluster refines the decision boundaries by learning the subgroups within a cluster. To obtain a final decision on detection, the k-Means and ID3 decision trees are combined using two rules: (1) the nearest neighbor rule; and (2) the nearest consensus rule. The performance of the K-Means+ID3 is demonstrated over three data sets: (1) network anomaly data, (2) Duffing equation data, and (3) mechanical system data, which contain measurements drawn from three distinct application domains of computer networks, an electronic circuit implementing a forced Duffing equation, and a mechanical mass beam system subjected to fatigue stress, respectively. Results show that the detection accuracy of the K-Means+ID3 method is as high as 96.24 percent on network anomaly data; the total accuracy is as high as 80.01 percent on mechanical system data; and 79.9 percent on Duffing equation data. Further, the performance of K-Means+ID3 is compared with individual k-Means and ID3 methods implemented for anomaly detection. The second method "dependence tree based anomaly detection" performs supervised anomaly detection using the Bayes classification rule. The class conditional probability densities in the Bayes classification rule are approximated by dependence trees, which represent second-order product approximations of probability densities. We derive the theoretical relationship between dependence tree classification error and Bayes error rate and show that the dependence tree approximation minimizes an upper bound on the Bayes error rate. To improve the classification performance of dependence tree based anomaly detection, we use supervised and unsupervised Maximum Relevance Minimum Redundancy (MRMR) feature selection method to select a set of features that optimally characterize class information. We derive the theoretical relationship between the Bayes error rate and the MRMR feature selection criterion and show that MRMR feature selection criterion minimizes an upper bound on the Bayes error rate. The performance of the dependence tree based anomaly detection method is demonstrated on the benchmark KDD Cup 1999 intrusion detection data set. Results show that the detection accuracies of the dependence tree based anomaly detection method are as high as 99.76 percent in detecting normal traffic, 93.88 percent in detecting denial-of-service attacks, 94.88 percent in detecting probing attacks, 86.40 percent in detecting user-to-root attacks, and 24.44 percent in detecting remote-to-login attacks. Further, the performance of dependence tree based anomaly detection method is compared with the performance of naive Bayes and ID3 decision tree methods as well as with the performance of two anomaly detection methods reported in recent literature.