Proposed approach to detect distributed denial of service attacks in software defined network using machine learning algorithms

  • Authors

    • Shruti Banerjee
    • Partha Sarathi Chakraborty
    • . .
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10488
  • Software Defined Network, Distributed Deninal-of-Service Attack, Machine Learning Algorithm.
  • SDN (Software Defined Network) is rapidly gaining importance of ‘programmable network’ infrastructure. The SDN architecture separates the Data plane (forwarding devices) and Control plane (controller of the SDN). This makes it easy to deploy new versions to the infrastructure and provides straightforward network virtualization. Distributed Denial-of-Service attack is a major cyber security threat to the SDN. It is equally vulnerable to both data plane and control plane. In this paper, machine learning algorithms such as Naïve Bayesian, KNN, K Means, K-Medoids, Linear Regression, use to classify the incoming traffic as usual or unusual. Above mentioned algorithms are measured using the two metrics: accuracy and detection rate. The best fit algorithm is applied to implement the signature IDS which forms the module 1 of the proposed IDS. Second Module uses open connections to state the exact node which is an attacker and to block that particular IP address by placing it in Access Control List (ACL), thus increasing the processing speed of SDN as a whole. 

  • References

    1. [1] Peng Zhang, Huanzhao Wang, Chengchen Hu, and Chuang Lin, “On Denial of Service Attacks in Software Defined Networksâ€, Network Forensics and Surveillance for Emerging Networks IEEE Network, Nov-Dec, 2016.

      [2] Moreno Ambrosin, Mauro Conti, Fabio De, Nishanth Devarajan, “Ampliï¬ed Distributed Denial of Service Attack in Software Defined Networkingâ€, IEEE 2016.

      [3] Lohit Barki, Amrit Shidling, Nisharani Meti, Narayan D G and Mohammed Moin Mulla, “Detection of Distributed Denial of Service Attacks in Software Defined Networksâ€, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, Sept. 21-24, 2016.

      [4] Raphael Durner, Claas Lorenz, Michael Wiedemann, Wolfgang Kellere, “Detecting and Mitigating Denial of Service Attacks against the Data Plane in Software Defined Networksâ€, IEEE 2017.

      [5] Damian Jankowski, and Marek Amanowicz, “On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networksâ€, International Journal of Electronics and Telecommunications, 2016.

      [6] Saurav Nanda, Faheem Zafari, Casimer DeCusatis, Eric Wedaa and Baijian Yan, “Predicting Network Attack Patterns in SDN using Machine Learning Approachâ€, IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2016.

      [7] S. Umarani, D. Sharmila, “Predicting Application Layer DDoS Attacks Using Machine Learning Algorithmsâ€, World Academy of Science, Engineering and Technology, IJCEACIE, Vol. 8, No.10, 2014.

      [8] Mohammad Reza Parsaei, Mohammad Javad Sobouti, Seyed Raouf khayami, and Reza Javidan, “Network Traffic Classification using Machine Learning Techniques over Software Defined Networks†IJACSA, Vol. 8, No.7, 2017.

      [9] LongTail, “LongTail Log Analysis Dashboardâ€. http://longtail.it.marist. edu/honey/dashboard.shtml. [Online; accessed 22-April-2016].

      [10] T. Padmapriya, V.Saminadan, “Performance Improvement in long term Evolution-advanced network using multiple imput multiple output techniqueâ€, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9, Sp-6, pp: 990-1010, 2017.

      [11] S.V.Manikanthan and V.Rama“Optimal Performance Of Key Predistribution Protocol In Wireless Sensor Networks†International Innovative Research Journal of Engineering and Technology ,ISSN NO: 2456-1983,Vol-2,Issue –Special –March 2017.

      [12] Harikishore Kakarla, Madhavi Latha M and Habibulla Khan, “Transition Optimization in Fault Free Memory Application Using Bus-Align Modeâ€, European Journal of Scientific Research, Vol.112, No.2, pp.237-245, ISSN: 1450-216x135 /1450-202x, October 2013.

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  • How to Cite

    Banerjee, S., Sarathi Chakraborty, P., & ., . (2018). Proposed approach to detect distributed denial of service attacks in software defined network using machine learning algorithms. International Journal of Engineering & Technology, 7(2.8), 472-476. https://doi.org/10.14419/ijet.v7i2.8.10488