Data Mining Method of Malicious Attack Based on Characteristic Frequency

Luo, Jia; Zhang, Chan

Aiming at the problem of high false alarm rate and failure rate in traditional data mining methods of malicious attacks, a data mining method of malicious attacks based on characteristic frequency is designed. Preprocess the original data in the data set, select the minimum attribute subset, use the discretization to process the unified data format, take the new subset as the input of feature frequency extraction of malicious attack data, extract the feature frequency according to the different protocols of malicious attack data transmission, integrate it into the value data mining algorithm, and use the spatial mapping principle to realize the malicious attack data excavate. The experimental results show that: compared with the traditional data mining method, the false alarm rate and failure rate of the designed malicious attack data mining method based on the feature frequency are reduced by 0.3 and 0.2 respectively, which shows that the method is more suitable for practical projects.