Evaluating Intrusion Sensitivity Allocation with Support Vector Machine for Collaborative Intrusion Detection

The aim of collaborative intrusion detection networks (CIDNs) is to provide better detection performance over a single IDS, through allowing IDS nodes to exchange data or information with each other. Nevertheless, CIDNs may be vulnerable to insider attacks, and there is a great need for deploying appropriate trust management schemes to protect CIDNs in practice. In this work, we advocate the effectiveness of intrusion sensitivity-based trust management model and describe an engineering way to automatically allocate the sensitivity values by using a support vector machine (SVM) classifier. To explore the allocation performance, we compare our classifier with several traditional supervised algorithms in the evaluation. We further investigate the performance of our enhanced trust management scheme in a real network environment under adversarial scenarios, and the experimental results indicate that our approach can be more effective in detecting insider attacks as compared with similar approaches.