Association RuleMining Frequent-Pattern-Based Intrusion Detection in Network

作     者：S.Sivanantham V.Mohanraj Y.Suresh J.Senthilkumar 

作者机构：Department of Information TechnologyAdhiyamaan College of EngineeringHosurTamilnaduIndia Department of Information TechnologySona College of TechnologySalemTamilnaduIndia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第2期

页      面：1617-1631页

核心收录：

学科分类：08[工学] 0837[工学-安全科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：IDS K-means frequent pattern tree false alert mining L1-norm 

摘      要：In the network security system,intrusion detection plays a significant *** network security system detects the malicious actions in the network and also conforms the availability,integrity and confidentiality of data informa-tion *** identification system can easily detect the false positive *** large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine *** research works have been *** issues in the existing algo-rithms are more memory space and need more time to execute the transactions of *** paper proposes a novel framework of network security Intrusion Detection System(IDS)using Modified Frequent Pattern(MFP-Tree)via K-means *** accuracy rate of Modified Frequent Pattern Tree(MFPT)-K means method infinding the various attacks are Normal 94.89%,for DoS based attack 98.34%,for User to Root(U2R)attacks got 96.73%,Remote to Local(R2L)got 95.89%and Probe attack got 92.67%and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.