Computer Vulnerability Detection and Early Warning Method Based on Data Mining Technology
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Tianyu Ren, Xiaohu Wang, Jiahan Dong, Guangxin Guo, Chao WANG
Computer security is a problem that has been attached great importance to in the process of computer development. Only by developing a new detection vulnerability screening and early warning technology can we ensure the security of computer use. Therefore, this paper studies the computer vulnerability detection and early warning method based on data mining technology. Based on the Apriori algorithm of data mining technology, a hybrid detection method is proposed in this paper, which is based on data mining technology. In order to verify the method in this paper, we carry out the simulation experiment through the experimental simulation platform, and select the other three methods as the control, and analyze the detection time, detection rate, false alarm rate and false negative rate. The results show that the detection accuracy of static detection technology is 63.45%, false positive rate is 36.55%, false positive rate is 15.23%, dynamic detection technology detection accuracy rate is 71.64%, false positive rate is 28.36%, false positive rate is 14.32%, binary comparison technology detection accuracy rate is 82.36%, false positive rate is 17.64%, false positive rate is 11.63%, detection accuracy of this method is 95.72%, false positive rate is 95.72% 28% and 7. 32% respectively. From the simulation test results, we can see that the computer vulnerability detection and early warning technology based on data mining technology can ensure the running time and detection rate, and control the false detection rate strictly and accurately detect the results, so as to achieve the best detection effect.
Data mining technology, Computer vulnerabilities, Vulnerability screening and early warning, Apriori algorithm