An Improved Perceptron Tree Learning Model Based Intrusion Detection Approach

This paper dedicates to develop an improved perceptron tree (PT) learning model based intrusion detection approach. The binary tree structure of a PT enables the model to divide the intrusion detection problem into sub-problems and solve them in decreased complexity in different tree levels. The expert neural networks (ENNs) embedded in the internal nodes can be simplified by limiting the number of inputs and hidden neurons. The potential advantage of a PT is that the trained learning model is actually a ¿gray box¿ since each embedded simplified ENN can be interpreted into explicit rules easily. However, the whole structure of a PT is likely to be high complex, i.e., the trained PT is probably composed of a large number of internal nodes. In this case, the disjunctive description of the learned intrusion detection rules extracted from such PT is too complex to understand. The generalization ability of the detection approach may be depressed as well. In view of this situation, the structure of the trained PT needs to be fine pruned. The experimental results demonstrate that the proposed approach can achieve competitive detection accuracy as well as refined learning model structure.