The rapid development of malicious code has seriously affected network information security.Traditional malicious code detection methods do not clearly divide network behavior characteristics,causing the low recognition rate and high false positive rate of malicious attack code.Therefore,a malicious attack code detection method for communication network based on PSO-KM clustering analysis is researched.The specific content of malicious attack code in communication network is analyzed,and the network behavior is extracted from the flow trajectory of network layer,and the behavior characteristics are determined in the MFAB-NB framework.The initial processing center is selected by the normalization algorithm,and the behavior characteristics of the classified communication network are normalized to judge the attack speed and location.The whole process of communication network data transmission is followed up in real time,and the fitness function is applied to seek the updating optimal solution of malicious code.The feature set of malicious code data is constructed based on the PSO-KM clustering analysis technology,and the small batch calcu-lation method is used to allocate the weight of the feature cluster.The weighted average value is used as the distribution basis to de-tect the malicious attack code,realize the design of detection method.The experimental results show that under the application of this method,the correct recognition rate of malicious attack code detection can reach more than 95.0％,the highest value is close to 99.7％,and the false positive rate can be controlled within 0.4％,and it has better application value.
关键词
恶意攻击代码/通信网络/PSO-KM聚类分析/聚类权重/网络行为特征/行为优劣程度
Key words
malicious attack code/communication network/PSO-KM clustering analysis/cluster weight/network behavior char-acteristics/degree of good or bad behavior
维普
万方数据