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Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system

Abstract

In the recent developments in the cloud computing made it’s accessible by everyone and millions of people daily store their data in the cloud platform and utilize for various kind of need. In this situation, the common issue in the day-to-day usage is DDoS attack, which severally affects the availability of the resources or services. In this paper a new method is proposed to detect and defend against the DDoS attacks using autonomous multi agent system and the agents use the particle swarm optimization among themselves to have strong communication and accurate decision making. DDoS attacks are detected using the multiple agents that communicate with each other and updates the coordinator agent. The current scenario is analyzed by the coordinator agent using the entropy and covariance methods to check for the DDoS attacks. During this stage the monitoring agent will be in live and keeps eye on the cloud resources and networking. If anything happens abnormal it triggers the detection and recovery agents to act. The experimental result shows this proposed system gives the optimized performance and improved security in the cloud platform.

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References

  1. AbRahman, N.H., Glisson, W.B., Yang, Y., Choo, K.K.R.: Forensic-by-design framework for cyber-physical cloud systems. IEEE Cloud Comput. 3(1), 50–59 (2016)

    Article  Google Scholar 

  2. Zhong, M., Cassandras, C.G.: Asynchronous distributed optimization with event-driven communication. IEEE Trans. Autom. Control 55(12), 2735–2750 (2010)

    MathSciNet  Article  Google Scholar 

  3. Özçelik, İ., Brooks, R.R.: Cusum—entropy: an efficient method for DDoS attack detection. In: 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG), Istanbul, pp. 1–5 (2016)

  4. Herrero, E., Corchado, M., Pellicer, A., Abraham, A.: Hybrid multi agent-neural network intrusion detection with mobile visualization. Innov. Hybrid Intell. Syst. 44, 320–328 (2007)

    Article  Google Scholar 

  5. Mirkovic, J., Hussain, A., Fahmy, S., Reiher, P., Thomas, R.: Accurately measuring denial of service in simulation and test bed experiments. IEEE Trans. Dependable Secure Comput. 6(2), 81–95 (2009)

    Article  Google Scholar 

  6. Chen, Y., Hwang, K., Kwok Y.K.: Collaborative defense against periodic shrew DDoS attacks in frequency domain. ACM Trans. Inf. Syst. Secur. (2005)

  7. Zhang, M., Wang, L., Jajodia, S., Singhal, A., Albanese, M.: Network diversity: a security metric for evaluating the resilience of networks against zero-day attacks. IEEE Trans. Inf. Forensics Secur. 11(5), 1071–1086 (2016)

    Article  Google Scholar 

  8. Watson, M.R., Shirazi, N.U.H., Marnerides, A.K., Mauthe, A., Hutchison, D.: Malware detection in cloud computing infrastructures. IEEE Trans. Dependable Secure Comput. 13(2), 192–205 (2016)

    Article  Google Scholar 

  9. He, X., Dai, H., Ning, P.: Faster learning and adaptation in security games by exploiting information asymmetry. IEEE Trans. Signal Process. 64(13), 3429–3443 (2016)

    MathSciNet  Article  Google Scholar 

  10. Zargar, S.T., Joshi, J., Tipper, D.: A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Commun. Surv. Tutor. 15(4), 2046–2069 (2013)

    Article  Google Scholar 

  11. Zhou, C.V., Leckie, C., Karunasekera, S.: A survey of coordinated attacks and collaborative intrusion detection. Comput. Secur. 29(1), 124–140 (2010)

    Article  Google Scholar 

  12. Yu, S., Tian, Y., Guo, S., Wu, D.O.: Can we beat DDoS attacks in clouds? IEEE Trans. Parallel Distrib. Syst. 25(9), 2245–2254 (2014)

    Article  Google Scholar 

  13. Erhan, D., Anarım, E., Kurt, G.K.: DDoS attack detection using matching pursuit algorithm. In: 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, pp. 1081–1084 (2016)

  14. Xu, X., Sun, Y., Huang, Z.: Defending DDoS attacks using hidden markov models and cooperative reinforcement learning. Intell. Secur. Inform. 4430, 196–207 (2007)

    Google Scholar 

  15. Jin, C., Wang, H., Shin, K.G.: Hop-count filtering: an effective defense against spoofed DDoS traffic. In: 10th ACM conference on Computer & communication security, pp. 30–41 (2003)

  16. Yan, Q., Yu, F.R.: Distributed denial of service attacks in software-defined networking with cloud computing. IEEE Commun. Mag. 53(4), 52–59 (2015)

    Article  Google Scholar 

  17. Yan, Q., Huang, W., Luo, X.: A multi-level DDoS mitigation framework for the industrial Internet of things. IEEE Commun. Mag. 56(2), 30–36 (2018)

    Article  Google Scholar 

  18. Liu, X., Yuan, C., Yang, Z., Zhang, Z.: Mobile-agent-based energy-efficient scheduling with dynamic channel acquisition in mobile cloud computing. J. Syst. Eng. Electron. 27(3), 712–720 (2016)

    Article  Google Scholar 

  19. Colman-Meixner, C., Develder, C., Tornatore, M., Mukherjee, B.: A survey on resiliency techniques in cloud computing infrastructures and applications. IEEE Commun. Surv. Tutor. 18(3), 2244–2281 (2016)

    Article  Google Scholar 

  20. AlRashidi, M.R., El-Hawary, M.E.: A survey of particle swarm optimization applications in electric power systems. IEEE Trans. Evol. Comput. 13(4), 913–918 (2009)

    Article  Google Scholar 

  21. Liu, Z., Yin, X., Lee, H.J.: A new network flow grouping method for preventing periodic shrew DDoS attacks in cloud computing. In: 18th International Conference on Advanced Communication Technology (ICACT), Pyeongchang, pp. 66–69 (2016)

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Correspondence to R. Kesavamoorthy.

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Kesavamoorthy, R., Ruba Soundar, K. Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system. Cluster Comput 22, 9469–9476 (2019). https://doi.org/10.1007/s10586-018-2365-y

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  • DOI: https://doi.org/10.1007/s10586-018-2365-y

Keywords

  • Cloud computing
  • DDoS attack and detection
  • Multi agent system
  • Swarm intelligence