Jianwu ZHANG, Jiasen HUANG, Di ZHOU. Intrusion detection model based on fuzzy theory and association rules[J]. Telecommunications science, 2019, 35(5): 59-69.
DOI:
Jianwu ZHANG, Jiasen HUANG, Di ZHOU. Intrusion detection model based on fuzzy theory and association rules[J]. Telecommunications science, 2019, 35(5): 59-69. DOI: 10.11959/j.issn.1000-0801.2019077.
Intrusion detection model based on fuzzy theory and association rules
An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base
and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed
and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm
in addition
the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy
and low false positive rate and false negative rate.
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references
DENNING D E . An intrusion-detection model [J ] . IEEE Transactions on Software Engineering , 1987 , SE-13 ( 2 ): 222 - 232 .
AGRAWAL R , IMIELINSKI T , SWAMI A N . Mining association rules between sets of items in large databases [C ] // The 1993 ACM SIGMOD International Conference on Management of Data,May 26-28,1993,New York,USA . New York:ACM Press , 1993 : 207 - 216 .
AGRAWAL R , SRIKANT R . Fast algorithms for mining association rules [M ] . New York : ACM PressPress , 1994 : 487 - 499 .
ADITYA S P , HEMANTH M , LAKSHMIKANTH C K , et al . Effective algorithm for frequent pattern mining [C ] // International Conference on IoT and Application,May 19-20,2017,Nagapattinam,India . Piscataway:IEEE Press , 2017 .
LI L , LI Q , WU Y , et al . Mining association rules based on deep pruning strategies [J ] . Wireless Personal Communications , 2018 ( 2 ): 1 - 25 .
XIAO M , YIN Y , ZHOU Y , et al . Research on improvement of apriori algorithm based on marked transaction compression [C ] // Advanced Information Technology,Electronic and Automation Control Conference,March 25-26,2017,Chongqing,China . Piscataway:IEEE Press , 2017 : 1067 - 1071 .
HUANG Y L , LUO X X , LIN Q . An association rule mining scheme based on bit operation and reverse index [J ] . Telecommunications Science , 2015 , 31 ( 11 ): 81 - 86 .
CHANA G Y , CHUAA F F , LEEB C S . Fuzzy association rules vs fuzzy associative patterns in defending against Web service attacks [C ] // International Conference on Fuzzy Systems and Knowledge Discovery,August 13-15,2016,Changsha,China . Piscataway:IEEE Press , 2016 : 524 - 529 .
ZHANG C Q , XIE L C . Network intrusion detection method based on improved FCM and rule parameter optimization in cloud environment [J ] . Telecommunications Science , 2018 , 34 ( 1 ): 72 - 79 .
JIAO W , LI Q . Anomaly detection based on fuzzy rules [J ] . International Journal of Performability Engineering , 2018 , 14 ( 2 ): 376 - 385 .
MABU S , CHEN C , LU N , et al . An intrusion-detection model based on fuzzy class-association-rule mining using genetic network programming [J ] . IEEE Transactions on Systems Man &Cybernetics Part C , 2010 , 41 ( 1 ): 130 - 139 .
GAO N , GAO L , HE Y Y , et al . A lightweight intrusion detection model based on auto encoder network with feature reduction [J ] . Acta Electronica Sinica , 2017 ( 3 ): 730 - 739 .
KUMAR S . Classification and detection of computer intrusions [D ] . West Lafayette:Purdue University , 1995 .
MADBOULY A I , GODY A M , BARAKAT T M . Relevant feature selection model using data mining for intrusion detection system [J ] . International Journal of Engineering Trends &Technology , 2014 , 9 ( 10 ): 804 - 811 .