Abstract
The current cybersecurity situation is getting worse. In order to improve the accuracy of network security situation prediction, a network security situation prediction method based on BP neural network optimized by Seeker Optimization Algorithm (SOA) is proposed. The algorithm uses the four behavioral characteristics of SOA: the self-interest, altruism, pre-action and uncertainty reasoning to determine the search strategy, find the best fitness individual, obtain the optimal weight and threshold, and then assign value to the random initial weights and thresholds of BP neural network. After training the neural network, the predicted values are obtained. Finally, it is compared with the predicted values obtained by other two optimization algorithms. The experimental results show that this prediction algorithm has higher accuracy, smaller error and better stability.
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This paper is sponsored by key foundation of Science and Technology Development of Henan Province (No. 142102210081) and National Natural Science Foundation of China (No. 61772477).
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Zhang, R., Liu, M., Zhang, Q., Cai, Z. (2020). A Network Security Situation Prediction Algorithm Based on BP Neural Network Optimized by SOA. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_37
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