A Network Security Situation Prediction Algorithm Based on BP Neural Network Optimized by SOA

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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