Phases vs. Levels using Decision Trees for Intrusion Detection Systems
This work addresses intrusion detection for computer network security, but it is incremental as it compares existing model approaches without introducing new methods.
The paper compared Phase-model and Level-model approaches for intrusion detection systems using decision tree techniques, finding that the Phase approach achieved higher classification rates in both New Attacks and Data Partitioning techniques.
Security of computers and the networks that connect them is increasingly becoming of great significance. Intrusion detection system is one of the security defense tools for computer networks. This paper compares two different model Approaches for representing intrusion detection system by using decision tree techniques. These approaches are Phase-model approach and Level-model approach. Each model is implemented by using two techniques, New Attacks and Data partitioning techniques. The experimental results showed that Phase approach has higher classification rate in both New Attacks and Data Partitioning techniques than Level approach.