Ensemble classifier for misuse detection using N-gram feature vectors through operating system call traces

Abstract

This paper presents development and performance evaluation of a host-based misuse intrusion detection system. The misuse detection system employs an ensemble design for classification and N-gram feature extraction methodology for preprocessing the raw ADFA-LD system call trace data. Inputs to the e nsemble classifier are fixed size patterns whose attributes are N-grams derived from operating system call traces. The ADFA-LD data set entails system call traces generated by a number of concurrently executing applications on the Linux operating system environment. In addition to the normal or attack-free operating mode, six different attacks are modeled in the data set. Two filters are used to capture the unique signatures of each class and also to reduce the dimensionality of input patterns. The most frequent unique features in the form of N-grams from each class are extracted from the raw trace data. Then, to eliminate the effect of noise, most frequent patterns regardless of uniqueness are extracted based on the statistical attributes of their occurrence frequencies. The SMOTE algorithm is used to balance the classes in terms of pattern counts. The classifier design is based on majority voting ensemble with base classifiers including naïve Bayes, support vector machine, PART, decision tree and random forest. The study considers both binary and multi class problems. In the case of binary class problem, the two classes are the “normal” and the “attack” where the latter is formed by merging all of the trace data for all six different attack signatures into a single one. In the multiclass case, normal and six attack classes are each considered separately. A simulation study was performed to evaluate the performance of the proposed host-based misuse detection system. Multiple performance indicators and metrics including confusion matrix, true positive/negative, and false positive/negative rates were recorded. The proposed host-based misuse detection system demonstrated very high performance for detecting the attacks for the binary class problem although its performance for the multi-class case will benefit from further improvement.