Intelligent Intrusion Detection System Combining Misuse Detection and Anomaly Detection Using Random Forest Algorithm
Keywords:
Random forest, Intrusion Detection, Clustering,, Classification, Outliers
Abstract
Intelligent Intrusion Detection is a system that has the ability to provide security to data. It compares the existing attacks with the new attacks. This helps to secure data from the intruders and also monitors the intruder’s system. The IDS generates alarm when a malicious attack is detected [4]. In this research, an Intelligent Intrusion Detection System is proposed which detects attacks with high accuracy and in fast response time. The proposed system has two phases, 1) Clustering Technique which process large amounts of data efficiently to identify outliers 2) A Classification Algorithm that classifies attacks and also avoids high false positives.
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References
[1] Wikipedia, Convolutional neural networks, available at https://en.wikipedia.org/wiki/ convolutional neural networks.
[2] “Using Convolutional Neural Networks to Network Intrusion Detection for Cyber Threats” Wen-Hui Lin1, Hsiao-Chung Lin, Ping Wang, Bao-Hua Wu, Jeng-Ying Tsai, 2018.
[3] S. A. Hofmeyr and A. S. Forrest, "Architecture for an Artificial Immune System," Evolutionary Computation, vol. 8, no. 4, pp. 443-473, December 2000.
[4] S. Forrest, A. S. Perelson, L. Allen and R. Cherukuri, "Self-Nonself Discrimination in a Computer," in proceedings of the 1994 IEEE Symposium on Security and Privacy.
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[8] C. Chen, S. Mabu, K. Shimada and K. Hirasawa, ”Network Intrusion Detection using Class Association Rule Mining Based on Genetic Network Programming”, IEEJ Transactions on Electrical and Electronic Engineering(Conditionally Accepted)
[9] C. Chen, S. Mabu, C. Yue, K. Shimada and K. Hirasawa, ”Network Intrusion Detection using Fuzzy Class Association Rule Mining Based on Genetic Network Programming”, In Proc. of the IEEE Internatinal Conference on Systems, Man and Cybernetics, 2009 (Submitted).
[10] “A New Intrusion Detection System based on Fast Learning Network and Particle Swarm Optimization”, Mohammed Hasan Ali, Bahaa Abbas Dawood Al Mohammed, Alyani Ismail and Mohamad Fadli Zolkipli, 2018.
[2] “Using Convolutional Neural Networks to Network Intrusion Detection for Cyber Threats” Wen-Hui Lin1, Hsiao-Chung Lin, Ping Wang, Bao-Hua Wu, Jeng-Ying Tsai, 2018.
[3] S. A. Hofmeyr and A. S. Forrest, "Architecture for an Artificial Immune System," Evolutionary Computation, vol. 8, no. 4, pp. 443-473, December 2000.
[4] S. Forrest, A. S. Perelson, L. Allen and R. Cherukuri, "Self-Nonself Discrimination in a Computer," in proceedings of the 1994 IEEE Symposium on Security and Privacy.
[5] M. S. Hoque, M. A. Mukit, M. A. N. Bikas, and M. S. Hoque, ``An implementation of intrusion detection system using genetic algorithm,'' Int. J. Netw. Secur. Appl., vol. 4, no. 2, pp. 109_120, 2012.
[6] J. Frank, ``Artificial intelligence and intrusion detection: Current and future directions,'' in Proc. 17th Nat. Comput. Secur. Conf., Baltimore, MD, USA, Oct. 1994.
[7] KDD Cup 1999. Available on: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, Ocotber 2007.
[8] C. Chen, S. Mabu, K. Shimada and K. Hirasawa, ”Network Intrusion Detection using Class Association Rule Mining Based on Genetic Network Programming”, IEEJ Transactions on Electrical and Electronic Engineering(Conditionally Accepted)
[9] C. Chen, S. Mabu, C. Yue, K. Shimada and K. Hirasawa, ”Network Intrusion Detection using Fuzzy Class Association Rule Mining Based on Genetic Network Programming”, In Proc. of the IEEE Internatinal Conference on Systems, Man and Cybernetics, 2009 (Submitted).
[10] “A New Intrusion Detection System based on Fast Learning Network and Particle Swarm Optimization”, Mohammed Hasan Ali, Bahaa Abbas Dawood Al Mohammed, Alyani Ismail and Mohamad Fadli Zolkipli, 2018.
Published
2022-01-02
How to Cite
Jeyanthi, & DR. B.INDRANI. (2022). Intelligent Intrusion Detection System Combining Misuse Detection and Anomaly Detection Using Random Forest Algorithm. INTERNATIONAL JOURNAL OF RESEARCH PEDAGOGY AND TECHNOLOGY IN EDUCATION AND MOVEMENT SCIENCES, 10(3), 18-33. Retrieved from https://ijems.net/index.php/ijem/article/view/200
Section
Research Articles
