Backtracking analysis of correlated data based on improved Apriori algorithm for malicious traffic detection in the Internetof Things
In response to the importance of detecting malicious traffic and backtracking associated data in the Internet of Things,the Apriori algorithm is studied for association rule mining,and the shortcomings of the Apriori algorithm are improved to construct a module for malicious traffic detection and backtracking associated data.The analysis and testing of algorithm and module performance show that when the transaction library is large,the execution time of the improved Apriori algorithm is significantly shorter than that of the traditional Apriori algorithm;When the number of transaction libraries is 1000,the former is 30.3 seconds faster than the latter.As the number of transaction libraries increases,the efficiency of the improved Apriori algorithm is significantly better than that of the classic Apriori algorithm.The system constructed by the research method has a high detection rate and a low false detection rate,with remote command control having the highest detection rate and the lowest false detection rate,which are 90.60％and 5.7％,respec-tively.And it can perform correlation data backtracking analysis on some malicious behaviors.The research on malicious traffic detec-tion and associated data backtracking in the Internet of Things has a good effect on protecting the healthy development of the Internet of Things.
internet of thingsmalicious traffic detectionapriori algorithmrelated data backtracking
万方数据
维普
