Network Intrusion Detection Technology based on Improved C-means Clustering Algorithm

Abstract

Current intrusion detection systems have low detection rate and high false positive rate for new intrusion types. This article applied PSO in network security area, a novel intrusion detection method based on chaos Particle Swarm Optimization and Fuzzy C-Means Clustering is proposed in order to solve the problem of FCM which is much more sensitive to the initialization and easier to fall into local optimization. This method can quickly obtain global optimal clustering and can detect unknown intrusions efficiently, it does not need to classify the training data sets with artificial or other methods. The experimental results show that this method can detect unknown intrusions with lower false positive rate and higher true positive rate

Improvement and Validation of the BOAT Algorithm

“…Then we can receive the conclusion at the leaf node. The main current decision tree algorithms include ID3 [10], SLIQ, SPRINT, PUBLIC, RAINFOREST, BOAT, and etc. They have differences in attribute test technique, decision tree structure, cutting method and time while processing the big data.…”