Abstract
The information security of computer network has been threatened unprecedentedly recently. A more efficient and accurate method is needed to detect malicious code, but the traditional algorithm only detects malicious code to a certain extent because it uses manual feature analysis. In this paper, a malicious code feature extraction method based on particle swarm optimization K-means clustering analysis algorithm is proposed. The fitness function is used to judge the quality of the particles. After the convergence of the particle swarm optimization algorithm, the k-means algorithm is continued to perform after inheriting the global optimal position, and finally the clustering results are obtained. This paper compares the missed detection rate and accuracy of the algorithm. The results indicates that the proposed algorithm has higher accuracy and lower missed detection rate than the other two traditional clustering algorithms.
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Liu, B. (2020). Computer Network Information Security Protection Strategy Based on Clustering Algorithms. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1146. Springer, Cham. https://doi.org/10.1007/978-3-030-43306-2_1
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DOI: https://doi.org/10.1007/978-3-030-43306-2_1
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