An Intrusion Detection Model Based on Mining Maximal Frequent Itemsets Over Data Streams

2012 ◽  
Vol 11 (1) ◽  
pp. 561-565
Author(s):  
Yimin Mao ◽  
Zhigang Chen ◽  
Lumin Yang ◽  
Junfeng Man ◽  
Lixin Liu
Keyword(s):  
Intrusion Detection ◽  
Data Streams ◽  
Frequent Itemsets ◽  
Detection Model ◽  
Model Based ◽  
Maximal Frequent Itemsets

An Intrusion Detection Model Based on Mining Maximal Frequent Itemsets over Data Streams

2013 ◽  
Vol 339 ◽  
pp. 341-348
Author(s):  
Yi Min Mao ◽  
Xiao Fang Xue ◽  
Jin Qing Chen
Keyword(s):  
Intrusion Detection ◽  
Data Streams ◽  
Intrusion Detection System ◽  
Behavior Pattern ◽  
Detection System ◽  
Response Speed ◽  
Frequent Itemsets ◽  
Abnormal Behavior ◽  
Data Set ◽  
Maximal Frequent Itemsets

Ming association rules have been proved as an important method to detect intrusions. To improve response speed and detecting precision in the current intrusion detection system, this papers proposes an intrusion detection system model of MMFIID-DS. Firstly, to improve response speed of the system by greatly reducing search space, various pruning strategies are proposed to mine the maximal frequent itemsets on trained normal data set, abnormal data set and current data streams to establish normal and abnormal behavior pattern as well as user behavior pattern of the system. Besides, to improve detection precision of the system, misuse detection and anomaly detection techniques are combined. Both theoretical and experimental results indicate that the MMFIID-DS intrusion detection system is fairly sound in performance.

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