INTRODUCING A NEW INTRUSION DETECTION METHOD IN THE SDN NETWORK TO INCREASE SECURITY USING DECISION TREE AND NEURAL NETWORK
- Hits: 1431
Volume 2 (2), December 2019, Pages 97-112
Ebrahim Zaheri Abdevand1, Shamsollah Ghanbari1, Zhanat Umarova2, Zhalgasbek Iztayev2
Computer networks are difficult to use due to the large number of devices such as router, switch, hop, and many sophisticated security management protocols, but in networks defined with integrated management and configuration software, software-based networks are nowadays important. They are high-end and will become one of the most used and important communication tools in the IT world in the future. In these networks, like all other networks, data security and protection is crucial because a network that is not secure will not work, in this paper, we present a new method of intrusion detection in this network, which consists of two parts: training and testing. Looking to determine if the network is normal or not? By checking the output of these two categories, the current status of the network is determined. The proposed method uses decision tree and neural network. In each first class of tree, the classification of abnormal data is classified and in the second class, the norm data is in decision tree. The output of the decision tree is neural network input Shows that the proposed method performs well.
SDN network, security, intrusion detection
Ajaeiya, G. A., Adalian, N., Elhajj, I. H., Kayssi, A., & Chehab, A. (2017, July). Flow-based intrusion detection system for sdn. In 2017 IEEE Symposium on Computers and Communications (ISCC) (pp. 787-793). IEEE.
Alghuried, A. (2017). A model for anomalies detection in internet of things (IoT) using inverse weight clustering and decision tree.
Ali, S. T., Sivaraman, V., Radford, A., & Jha, S. (2015). A survey of securing networks using software defined networking. IEEE transactions on reliability, 64 (3), 1086-1097.
Boero, L., Marchese, M., & Zappatore, S. (2017, September). Support vector machine meets software defined networking in ids domain. In 2017 29th International Teletraffic Congress (ITC 29) (Vol. 3, pp. 25-30). IEEE.
Bozakov, Z., & Papadimitriou, P. (2014, May). Towards a scalable software-defined network virtualization platform. In 2014 IEEE Network Operations and Management Symposium (NOMS) (pp. 1-8). IEEE.
Campbell, C., & Ying, Y. (2011). Learning with support vector machines. Synthesis lectures on artificial intelligence and machine learning, 5 (1), 1-95.
Cup, K. D. D. (1999). Dataset. available at the following website http://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html, 72.
Dangovas, V., & Kuliesius, F. (2014, January). SDN-driven authentication and access control system. In The International Conference on Digital Information, Networking, and Wireless Communications (DINWC) (p. 20). Society of Digital Information and Wireless Communication.
Dang-Van, T., & Truong-Thu, H. (2017). A multi-criteria based software defined networking system Architecture for DDoS-attack mitigation. REV Journal on Electronics and Communications, 6 (3-4).
Dotcenko, S., Vladyko, A., & Letenko, I. (2014, February). A fuzzy logic-based information security management for software-defined networks. In 16th International Conference on Advanced Communication Technology (pp. 167-171). IEEE.
Giotis, K., Argyropoulos, C., Androulidakis, G., Kalogeras, D., & Maglaris, V. (2014). Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments. Computer Networks, 62, 122-136.
Hong, S., Xu, L., Wang, H., & Gu, G. (2015, February). Poisoning Network Visibility in Software-Defined Networks: New Attacks and Countermeasures. In NDSS (Vol. 15, pp. 8-11).
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).
Kim, H., & Feamster, N. (2013). Improving network management with software defined networking. IEEE Communications Magazine, 51 (2), 114-119.
Kim, J., Firoozjaei, M. D., Jeong, J. P., Kim, H., & Park, J. S. (2015, October). SDN-based security services using interface to network security functions. In 2015 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 526-529). IEEE.
Kreutz, D., Ramos, F., Verissimo, P., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2014). Software-defined networking: A comprehensive survey. arXiv preprint arXiv:1406.0440.
Narantuya, J. (2015). Distributed Sampling Algorithm for Intrusion Detection in SDN Environment’ (Doctoral dissertation, Master Thesis. Gwangju Institute of Science and Technology).
Narayanan, R., Lin, G., Syed, A. A., Shafiq, S., & Gilani, F. (2013, October). A framework to rapidly test SDN use-cases and accelerate middlebox applications. In 38th Annual IEEE Conference on Local Computer Networks (pp. 763-770). IEEE.
Nunes, B. A. A., Mendonca, M., Nguyen, X. N., Obraczka, K., & Turletti, T. (2014). A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Communications Surveys & Tutorials, 16 (3), 1617-1634.
Porras, P. A., Cheung, S., Fong, M. W., Skinner, K., & Yegneswaran, V. (2015, February). Securing the Software Defined Network Control Layer. In NDSS.
Sayeed, M. A., Sayeed, M. A., & Saxena, S. (2015, September). Intrusion detection system based on Software Defined Network firewall. In 2015 1st International Conference on Next Generation Computing Technologies (NGCT) (pp. 379-382). IEEE.
Scott-Hayward, S. (2015, April). Design and deployment of secure, robust, and resilient SDN Controllers. In Proceedings of the 2015 1st IEEE conference on network Softwarization (NetSoft) (pp. 1-5). IEEE.
Scott-Hayward, S., Natarajan, S., & Sezer, S. (2015). A survey of security in software defined networks. IEEE Communications Surveys & Tutorials, 18 (1), 623-654.
Van Trung, P., Huong, T. T., Van Tuyen, D., Duc, D. M., Thanh, N. H., & Marshall, A. (2015, October). A multi-criteria-based DDoS-attack prevention solution using software defined networking. In 2015 International Conference on Advanced Technologies for Communications (ATC) (pp. 308-313). IEEE.
Wang, H., Xu, L., & Gu, G. (2015, June). Floodguard: A dos attack prevention extension in software-defined networks. In 2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (pp. 239-250). IEEE.