Malicious URL Classification Model Based on Improved Sparrow Search Algorithm

With the development of the Internet, malicious URL attacks have shown a complex and changeable situation, and have always been one of the main factors that threaten network security. In order to detect malicious URLs more effectively, a random walk strategy is proposed to improve the Sparrow search algorithm, which is compared and analyzed with the Sparrow search algorithm, the gray wolf optimization algorithm and the whale optimization algorithm. After a benchmark test function experiment, it is verified that the improved sparrow search algorithm has the characteristics of fast convergence speed and high optimization precision. In order to further verify the practicability of the improved algorithm, the optimal solution of the random walk strategy optimization sparrow search algorithm is used as the initial neuron connection weight and threshold information of the BP neural network and applied to the classification of malicious URLs. The results show that the random walk strategy optimization sparrow search algorithm has a certain ability to optimize the BP network, which can improve the classification accuracy of malicious URLs.