基于QPSO小波神经网络的网络异常检测

Network anomal detection wavelet neural network based on QPSO

为了提高网络异常检测中,对异常状态的检测率,降低对正常状态的误判率,提出一种基于量子粒子群优化算法训练小波神经网络进行网络异常检测的新方法。利用量子粒子群优化算法(QPSO)训练小波神经网络,将小波神经网络(WNN)中的参数组合作为优化算法中的一个粒子,在全局空间中搜索具有最优适应值的参数向量。实验数据采用KDD CUP99数据集,实验结果表明:该学习算法与传统的梯度下降法(GD)和粒子群算法(PSO)相比,收敛速度快,具有更好的全局收敛性,提高了异常检测的准确性,同时该方法对于新的异常也有较高检测率。

In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection, a novel method of network anomaly detection based on constructing wavelet neural network （WNN） using quantum-behaved particle swarm optimization （QPSO） algorithm is proposed. The WNN is trained by QPSO. A multidimensional vector composed of WNN parameters is regarded as a particle in learning algorithm. The parameter vector, which has a best adaptation value, is searched globally. The well-known KDD CUP 99 Intrusion Detection Data Set is used as the experimental data. Experimental result on KDD 99 intrusion detection datasets shows that this learning algorithm has more rapid convergence, better global convergence ability compared with the traditional gradient descent （GD） algorithm and particle swarm optimization （PSO）, and the accuracy of anomaly detection is enhanced. It also shows the remarkable ability of this novel algorithm to detect new type of attacks.