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1篇 您的检索式：作者名="S.Sivanantham"

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		题名	作者	年代	出处	被引量
	1	Association RuleMining Frequent-Pattern-Based Intrusion Detection in Network显示文摘In the network security system,intrusion detection plays a signiﬁcant role.The network security system detects the malicious actions in the network and also conforms the availability,integrity and conﬁdentiality of data informa-tion resources.Intrusion identiﬁcation system can easily detect the false positive alerts.If large number of false positive alerts are created then it makes intrusion detection system as difﬁcult to differentiate the false positive alerts from genuine attacks.Many research works have been done.The issues in the existing algo-rithms are more memory space and need more time to execute the transactions of records.This paper proposes a novel framework of network security Intrusion Detection System(IDS)using Modiﬁed Frequent Pattern(MFP-Tree)via K-means algorithm.The 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.	S.Sivanantham V.Mohanraj Y.Suresh J.Senthilkumar	2023	Computer Systems Science & Engineering2023，44，2：	0

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