Intrusion Detection Using Tree Based Classifiers
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2020 by IJCTT Journal | ||
| Volume-68 Issue-2 |
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| Year of Publication : 2020 | ||
| Authors : Ashalata Panigrahi, Manas Ranjan Patra | ||
| DOI : 10.14445/22312803/IJCTT-V68I2P109 |
MLA Style:Ashalata Panigrahi, Manas Ranjan Patra "Intrusion Detection Using Tree Based Classifiers" International Journal of Computer Trends and Technology 68.2 (2020):59-63.
APA Style: Ashalata Panigrahi, Manas Ranjan Patra (2020). Intrusion Detection Using Tree Based Classifiers International Journal of Computer Trends and Technology, 68(2),59-63.
Abstract
Growing cyber-crimes have become a serious concern for network users. It has become a real challenge for organizations to develop network security systems to protect data from all kinds of illegal access. Since intruders keep applying different techniques to break the security barriers, the techniques to counter such attacks are also being developed by the researchers. In this work, a model has been proposed for building an effective intrusion detection system using tree based classification techniques, namely, BF Tree, FT, J48, NB Tree, Random Forest, and Random Tree. Further, three nature-inspired and two heuristic search based methods have been applied for selecting important features prior to the classification process. The performance of the model has been evaluated on the NSL-KDD dataset in terms of accuracy, precision, detection rate, and false alarm rate.
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Keywords
Best First Tree, Functional Tree, Naïve Bayes Tree, Particle Swarm Optimization, Heuristic Search.