Network Anomaly Detection Based on BQPSO-BN Algorithm

Abstract

As cloud computing becomes popular, intrusion detection has been focused again, since huge amount of network attacks have increased the requirement of efficient network intrusion detection techniques. Currently, lots of methods are used to solve this issue, but lower detection rate of these original models cannot satisfy complex Internet environment. In this paper, we propose a novel intrusion detection model–Bayesian Network-based binary quantum-behaved particle swarm optimization (BQPSO-BN). Since the classical QPSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space, we redefine the position vector and the distance between two positions, and adjust the iterative equations of QPSO to binary search space. Experiment results with KDD’99 dataset show that BQPSO-BN is an efficient and effective algorithm and has better convergence speed compared with BPSO-BN and GA-BN models.

Keywords:

• Bayesian network
• Genetic algorithm
• Network anomaly detection
• Quantum-behaved particle swarm optimization

Additional information

Notes on contributors

Yuan Liu

Yuan Liu is a Professor in the School of Digital Media at the Jiangnan University. He received his M.S.(1999) from the Department of Automation at Wuxi University of Light Industry, and a B.S. degree (1987)in Electrical Engineering from the Fudan University, Shanghai. His primary research interests are in network security, network flow and the software of digit media. E-mail: [email protected]

Ruhui Ma

Ruhui Ma received his Ph.D.degree in computer science from Shanghai Jiao Tong University, China, in 2011. He received the B.S. and M.S. degree at School of Information and Engineering from Jiangnan University in 2006 and 2008, China, respectively. He is currently an assistant professor with the Faculty of Computer Science, Shanghai Jiao Tong University (Shanghai, China). His main research interests are in virtual machines, computer architecture and compiling. E-mail: [email protected]