Wavelet Fuzzy Neural Network Based on Modified QPSO for Network Anomaly Detection

Neural network (NN) employed to settle network anomaly has become prevalent. However, traditional training algorithm for NN is not optimum, that is, often suboptimum, and encountering complicated network anomaly, an adaptive yet efficient NN or hybrid NN model should be better considered. Therefore, this paper proposes a novel network anomaly detection method employing wavelet fuzzy neural network (WFNN) to use modified Quantum-Behaved Particle Swarm Optimization (QPSO). In this paper, wavelet transform is applied to extract fault characteristics from the anomaly state. Fuzzy theory and neural network are employed to fuzzify the extracted information. Wavelet is then integrated with fuzzy neural network to form the wavelet fuzzy neural network (WFNN). The Quantum-Behaved Particle Swarm Optimization, which outperforms other optimization algorithm considerably on its simple architecture and fast convergence, has previously applied to solve optimum problem. However, the QPSO also has its own shortcomings. So, there exists a modified QPSO which is used to train WFNN in this paper. Experimental result on KDD99 intrusion detection datasets shows that this WFNN using the novel training algorithm has high detection rate while maintaining a low false positive rate.