Vol.44, No.2, 2023, pp.1617-1631, doi:10.32604/csse.2023.025893
Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network
  • S. Sivanantham1,*, V. Mohanraj2, Y. Suresh2, J. Senthilkumar2
1 Department of Information Technology, Adhiyamaan College of Engineering, Hosur, Tamilnadu, India
2 Department of Information Technology, Sona College of Technology, Salem, Tamilnadu, India
* Corresponding Author: S. Sivanantham. Email:
Received 08 December 2021; Accepted 16 February 2022; Issue published 15 June 2022
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 information 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 algorithms 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 in finding 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.
IDS; K-means; frequent pattern tree; false alert; mining; L1-norm
Cite This Article
S. Sivanantham, V. Mohanraj, Y. Suresh and J. Senthilkumar, "Association rule mining frequent-pattern-based intrusion detection in network," Computer Systems Science and Engineering, vol. 44, no.2, pp. 1617–1631, 2023.
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