Improvement of Quantum Genetic Algorithms and Application of DDoS Attack Detection

Abstract

Aiming at the problem of insufficient searching ability of Quantum Genetic Algorithms (QGA), this paper proposes a method to improve QGA by dynamically changing the rotation angle of quantum revolving gates. Thirteen typical standard functions are used to test the improved QGA, and to further verify the improved QGA. In this paper, a DDoS attack detection model based on quantum genetic optimization BP neural network (DQGA-BP) is constructed. The model combines improved quantum genetic algorithm with BP neural network, and uses KDD-Cup 1999 data set (9-week network connection collected from the USAF LAN) to detect DDoS attacks. It effectively improves the accuracy of DDoS attack detection. The experimental results show that the improved quantum genetic algorithm has faster convergence speed and stronger optimization ability. Under DDoS attack detection, the average detection rate of DQGA-BP is 0.51491% higher than that of the original quantum genetic optimization BP neural network (QGA-BP), and the average false alarm rate is 0.37%.