A novel hybrid intrusion detection method integrating anomaly detection with misuse detection

• The proposed method hierarchically integrates a misuse detection model and an anomaly detection model.
• We use the C4.5 decision tree algorithm for building a misuse detection model.
• We then decompose the normal training data into smaller subsets using the model.
• Next, we build multiple one-class SVM models for the decomposed subsets.
• This approach results in high detection performance and reduces the detection time complexity.

In this paper, a new hybrid intrusion detection method that hierarchically integrates a misuse detection model and an anomaly detection model in a decomposition structure is proposed. First, a misuse detection model is built based on the C4.5 decision tree algorithm and then the normal training data is decomposed into smaller subsets using the model. Next, multiple one-class SVM models are created for the decomposed subsets. As a result, each anomaly detection model does not only use the known attack information indirectly, but also builds the profiles of normal behavior very precisely. The proposed hybrid intrusion detection method was evaluated by conducting experiments with the NSL-KDD data set, which is a modified version of well-known KDD Cup 99 data set. The experimental results demonstrate that the proposed method is better than the conventional methods in terms of the detection rate for both unknown and known attacks while it maintains a low false positive rate. In addition, the proposed method significantly reduces the high time complexity of the training and testing processes. Experimentally, the training and testing time of the anomaly detection model is shown to be only 50% and 60%, respectively, of the time required for the conventional models.