NIDS using Random Forest and Random Tree

Authors

  • K. Mohanapriya Dept. of Computer Science, Government Arts College for Women-Krishnagiri
  • M. Savitha Devi Dept. of Computer Science, Periyar University Constituent College of Arts & Science-Harur

Keywords:

Intrusion Detection, Security, Intruder, Decision Tree, Ensemble Weak Learner

Abstract

Network Intrusion Detection Systems (NIDS) is the most important system in cyber security and it informs network administrators about policy violations. Identifying the network security violations and tells about where administrators to be improved. In Existing NIDS is designed to detect known network attacks. In this paper it is proposed to develop systematic methods for classifying intrusion detection. The key ideas are to use data mining techniques to discover network behaviour, anomalies and known Intrusions. Decision trees have been effectively used in NIDS but suffer from over sampling and the tree splitting being greedy locally. To overcome this some of the ensemble techniques like Random Forest, Random Trees and Ensemble Weak Learner Tree (EWL TREE) are used. Proposed technique reduces the number of trees required and also improves the precision and recall.

References

[1] Raghunath, B. R., &Mahadeo, S. N. (2008, July). Network intrusion detection system (NIDS).

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[6] Jones JM, Fielding A, Sullivan M (2006) Analyzing extinction risk in parrots using decision trees. BiodiversConserv 15(6):1993–2007.

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Published

2025-11-25

How to Cite

[1]
K. Mohanapriya and M. S. Devi, “NIDS using Random Forest and Random Tree”, Int. J. Comp. Sci. Eng., vol. 7, no. 17, pp. 43–46, Nov. 2025.

Issue

Vol. 7 No. 17 (2019): IJCSE Special Issue May Edition

Section

Research Article

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