MODELLING OF AN INTRUSION DETECTION SYSTEM USING C4.5 MACHINE LEARNING ALGORITHM
The increasing growth of wireless networking and new mobile computing devices has caused boundaries between trusted and malicious users to be blurred. The shift in security priorities from the network perimeter to information protection and user resources security is an open area for research which is concerned with the protection of user information’s confidentiality, integrity and availability. Intrusion detection systems are programs or software applications embedded in sophisticated devices to monitor the activities on networks or systems for security, policy or protocol violation or malicious activities detection. In this work, an intrusion detection model was proposed using C4.5 algorithm which was implemented with WEKA tool and RAPID MINER. The model showed good performance when trained and tested with validation techniques. Implementation of the proposed model was conducted on the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset, an improved version of KDD 99 dataset, which showed that the proposed model approach has an average detection rate of 99.62% and reduced false alarm rate of 0.38%
Crosby, G. V., Hester, L., & Niki, P. (2011). Location-aware, Trust-based Detection and Isolation of Compromised Nodes in Wireless Sensor Networks. International Journal of Network Security, Vol.12, No.2., 107-117.
Dhopte, S., & Tarapore, N. (2012). Design of Intrusion Detection System using Fuzzy Class-Association Rule Mining based on Genetic Algorithm. International Journal of Computer Applications. Volume 53– No.14 , 20-27. ISSN: 0975 – 8887.
Hofmann, M., & Klinkenberg, R. (2014). Rapid Miner: Data Mining Use Cases and Business Analytics Applications. Boca Raton: CRC Press.
Holmes, G., Donkin, A., & Witten, I. (2002). WEKA: a machine learning workbench. IEEE Xplore. Brisbane: IEEE.
Jabez, J., & Muthukumar, B. (2015). Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection Approach. International Conference on Intelligent Computing, Communication & Convergence. Procedia Computer Science 48 (pp. 338 – 346 ). India: Elsevier B.V.
Liu, Y., & Zhu, L. (2019). A new intrusion detection and alarm correlation technology based on neural network. EURASIP Journal on Wireless Communications and Networking.
Nayak, U., & Rao, U. H. (2014). The InfoSec Handbook. An Introduction to Information Security. Apress. ISBN: 978-1-4302-6383-8.
Patil, U., Gunjal, R., Gadhe, A., Kulkarni, R., & Mandlik, S. (2016). Network Intrusion Detection & Prevention System using Fuzzy Logic and Genetic Algorithm. International Journal of Innovative Research in Science and Engineering. Vol 2(3), 276-283. ISSN: 2454-9665.
Quinlan, R. J. (1993). C4.5: programs for machine learning. San Francisco: Morgan Kaufmann Publishers Inc.
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