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.

Key words: BP neural network, seeker optimization algorithm, simulated annealing algorithm, network security, situation prediction