International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 9, No. 1, 8 Jan. 2019
Cover page and Table of Contents: PDF (size: 490KB)
DDoS Attack Detection Using C5.0 Machine Learning Algorithm
Full Text (PDF, 490KB), PP.52-59
Views: 0 Downloads: 0
Author(s)
Hariharan. M
1,*
Abhishek H. K
1
B. G. Prasad
1
Index Terms
Denial of Service, DDoS, Machine Learning, Decision Tree, C5.0
Abstract
Distributed Denial of Service has always been an issue while dealing with network security. The potential of DDoS attacks is not limited by any security measures. This type of attack does not attempt to breach a security perimeter but aims to make the service unavailable to legitimate users. This is particularly an issue in private clouds as public clouds have sophisticated systems to prevent DDoS attacks. DDoS attacks can be used as a shield for other malicious activities. Open resource access model of the Internet is exploited by Distributed Denial of Service attackers. The main objective of this paper is to detect DDoS attacks using C5.0 machine learning algorithm and compare the results with other state of the art classifiers like Naïve Bayes classifier and C4.5 decision tree classifier. The focus is on an offline detection model.
Cite This Paper
Hariharan. M, Abhishek H. K, B. G. Prasad, "DDoS Attack Detection Using C5.0 Machine Learning Algorithm", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.9, No.1, pp. 52-59, 2019. DOI: 10.5815/ijwmt.2019.01.06
Reference
[1] Douligeris, C., & Mitrokotsa, A. (2004). DDoS attacks and defense mechanisms: classification and state-of-the-art. Computer Networks, 44(5), 643-666.
[2] Cheeseman, P. C., Self, M., Kelly, J., Taylor, W., Freeman, D., & Stutz, J. C. (1988, August). Bayesian Classification. In AAAI(Vol. 88, pp. 607-611).
[3] Suthaharan, S. (2016). Support vector machine. In Machine learning models and algorithms for big data classification (pp. 207-235). Springer, Boston, MA.
[4] Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.
[5] Quinlan, R. (2004). Data mining tools See5 and C5. 0.
[6] Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883.
[7] Zekri, M., El Kafhali, S., Aboutabit, N., & Saadi, Y. (2017, October). DDoS attack detection using machine learning techniques in cloud computing environments. In Cloud Computing Technologies and Applications (CloudTech), 2017 3rd International Conference of (pp. 1-7). IEEE.
[8] Bujlow, T., Riaz, T., & Pedersen, J. M. (2012, January). A method for classification of network traffic based on C5. 0 Machine Learning Algorithm. In Computing, Networking and Communications (ICNC), 2012 International Conference on(pp. 237-241). IEEE.
[9] Pandya, R., & Pandya, J. (2015). C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning. International Journal of Computer Applications, 117(16).
[10] R. Revathy, and R. Lawrance. (2017). “Comparative Analysis of C4.5 and C5.0 Algorithms on Crop Pest Data,” International Journal of Innovative Research in Computer and Communication Engineering(IJIRCCE) Vol. 5, Special issue 1, March 2017
[11] Liao, Q., Li, H., Kang, S., & Liu, C. (2015). Application layer DDoS attack detection using cluster with label based on sparse vector decomposition and rhythm matching. Security and Communication Networks, 8(17), 3111-3120.
[12] Xiao, P., Qu, W., Qi, H., & Li, Z. (2015). Detecting DDoS attacks against data center with correlation analysis. Computer Communications, 67, 66-74.
[13] Karimazad, R., & Faraahi, A. (2011, September). An anomaly-based method for DDoS attacks detection using RBF neural networks. In Proceedings of the international conference on network and electronics Engineering (Vol. 11, pp. 44-48).
[14] Zhong, R., & Yue, G. (2010, April). DDoS detection system based on data mining. In Proceedings of the 2nd International Symposium on Networking and Network Security, Jinggangshan, China (pp. 2-4).