The Research On Intrusion Detection Based On Quantum Genetic Algorithm Optimization BP Network

Intrusion detection is developed as a dynamic monitoring system to prevent or resist the intrusion of the security mechanism in the past decade.There are many intrusion detection models and methods, while the neural network and pattern recognition technologies enable the introduction of intelligent intrusion detection to become a hot research. Neural network has self-learning, adaptive capacity, as long as the system's audit data or network packets, neural networks can through self-study to extract the normal characteristics of the user or system activity patterns and detect unusual activity patterns of attack. These features make it applicate in intrusion detection very good.The most popular neural network learning algorithm is BP algorithm.However, it has the following drawbacks as the gradient descent property of the BP algorithm: the convergence speed is slow; the algorithm is incomplete and tends to get the local extreme; the algorithm can be oscillatory when the learning ratio is set too high; the algorithm is not robust and the network performance is sensive to the initial configuration of the network. All of these drawbacks can lead to the high omission rate and false alarm rate when BP algorithm is applied in instrusion detection. This paper presented an instrusion detection model based on quantum genetic algorithm and BP neural network, which can deal these phenomenoms propertly. The model takes advantage of the global search property of the quantum genetic algorithm and the exact local search characteristics of the BP network. The weight and the thresholds of the BP neural network is optimized by the quantum genetic algorithm.As basic quantum genetic algorithm has weeklocal search ability and is easy to premature, this paper also proposed an improved quantum genetic algorithm. Many aspects of the algorithm have been re-designed with considerately analysis including the quantum bit encoding, fitness function design, update quantum gates, quantum variations, etc.Experiments show that this method can be used to improve the efficiency and accuracy of intrusion detection.