基于量子遗传算法优化神经网络的入侵检测

针对入侵检测的效率及准确性问题，提出一种基于量子遗传算法优化神经网络的入侵智能检测模型，该模型基于量子遗传算法的全局搜索和神经网络局部精确搜索特性，将量子遗传算法和BP算法有机结合。利用改进的量子遗传算法优化BP神经网络的权重和阈值，使BP神经网络能快速准确地识别入侵，增强计算机网络安全。运用Matlab软件对该模型进行仿真。实验结果表明，与其他同类方法相比，该方法的检测率更高、误报率更低。

Intrusion Detection Based on Neural Network Optimized by Quantum Genetic Algorithm

To solve the problem of efficiency and veracity of intrusion detection,this paper presents an intrusion detection model based on quantum genetic algorithm and neural network.The model takes advantage of the global search property of the quantum genetic algorithm and the exact local search characteristics of the BP neural network,and combines quantum genetic algorithm and BP neural network.The weight and the thresholds of the BP neural network are optimized by the improved quantum genetic algorithm,so that the BP neural network enhances efficiency and veracity of intrusion detection,thereby improving network security.Matlab emulating experiments of this model show this method is better than other kinds of methods in detection rate and false alarm rate.