Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (8): 1171-1182.

### Prediction Algorithm for Network Security Situation based on BP Neural Network Optimized by SA-SOA

Ran Zhang*, Min Liu, Yifeng Yin, Qikun Zhang, and Zengyu Cai

1. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
• Submitted on  ;  Revised on  ; Accepted on
• Contact: *E-mail address: ranranzh@sina.com
• About author:Ran Zhang is an associate professor at Zhengzhou University of Light Industry. Her research interests include network information security, situation awareness, and cloud computing.Min Liu is a master's candidate at Zhengzhou University of Light Industry. Her research interests include network information security and situation prediction.Yifeng Yin is a professor at Zhengzhou University of Light Industry. His research interests include network information security and cryptography.Qikun Zhang is an associate professor at Zhengzhou University of Light Industry. His research interests include information security and cryptography. Zengyu Cai is an associate professor at Zhengzhou University of Light Industry. His research interests include information security.

Abstract: Network security situation prediction has been a major research focus in the field of network security in recent years. It can predict the future network security status and its changing trends based on existing network security data to provide guidance for network security administrators' selection of security strategies. In this paper, a network security situation prediction algorithm based on BP neural network optimized by SA-SOA is proposed. The algorithm uses the seeker optimization algorithm (SOA) to find the best fitness individual, obtains the optimal weight and threshold value, assigns them to the random initial threshold value and weight value of the BP neural network, and finally obtains the prediction value through the training of the BP neural network. To solve the problem that the seeker optimization algorithm is easy to fall into the local optimization and slow convergence in the later stage of the search, the simulated annealing algorithm (SA) is introduced into the seeker optimization algorithm. According to the Metropolis criterion of SA, the algorithm accepts the bad solution with a certain probability, which avoids falling into the trap of the local optimum and improves the global search ability of the algorithm. The experimental results show that this algorithm is more accurate and more stable than other prediction algorithms based on the improved BP neural network.