Distributed Denial of Service Attack Detection Using Ant Bee Colony and Artificial Neural Network in Cloud Computing

  • Uzma Ali
  • Kranti K. Dewangan
  • Deepak K. Dewangan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 652)

Abstract

Distributed Denial of Services (DDoS) attack is the one of the most dangerous threats in the cloud computing. A group of zombies tries to attack a single target so that the victim is not able to use the resources more, and it leads to shutting down the system. And the actual attacker is hard to trace. In the proposed method, we used a hybrid approach which is Artificial Bee Colony and Back Propagation Artificial Neural Network. The proposed method is used to detect the DDoS attack in cloud computing. Firstly, the Artificial Bee Colony selects the weights and thresholds on the basis of minimum mean square error. And these weights and thresholds are used to initialize Back Propagation Artificial Neural Network. And then the training is performed based on the Back Propagation technique. It increases the speed and accuracy of detecting the DDoS attack.

Keywords

Cloud computing Artificial Neural Network Back Propagation Artificial Bee Colony Distributed Denial of Services Attack 

References

  1. 1.
    Shikha, K.M., Sharma, R.: A review on DDoS attack and its detection and defence method. Int. J. Sci. Technol. Manag. (IJSTM), Landran, Punjab (2015)Google Scholar
  2. 2.
    Yu, S.: Distributed denial of services attack and defense. Springer (2014)Google Scholar
  3. 3.
    Vidya, V.: A review of DDoS attack in cloud computing. IOSR J. Comput. Eng. (IOSR-JCE) 16(5), 32–35 (2014)CrossRefGoogle Scholar
  4. 4.
    Aishwarya, R., Malliga, S.: Intrusion detection system-an efficient way to thwart against Dos/DDos attack in the cloud environment. In: IEEE International Conference on Recent Trends in Information Technology (2014)Google Scholar
  5. 5.
    Sahu, S.S., Pandey, M.: Distributed denial of service attacks: a review. Int. J. Mod. Educ. Comput. Sci. 65–71 (2014)Google Scholar
  6. 6.
    Mittal, A., Shrivastava, A.K., Manoria, M.: A review of DDOS attack and its countermeasures in TCP based networks. Int. J. Comput. Sci. Eng. Surv. (IJCSES) 2(4) (2011)Google Scholar
  7. 7.
    Subaira, A.S., Anitha, P.: A survey: network intrusion detection system based on data mining techniques. Int. J. Comput. Sci. Mob. Comput. 2(10), 145–153 (2013)Google Scholar
  8. 8.
    Kale, M., Choudhari, D.M.: DDOS attack detection based on an ensemble of neural classifier. IJCSNS Int. J. Comput. Sci. Netw. Secur. 14(7) (2014)Google Scholar
  9. 9.
    Qian, Q., Cai, J., Zhang, R.: Intrusion detection based on neural networks and artificial bee. In: IEEE ICIS, Taiyun, China (2014)Google Scholar
  10. 10.
    Jaggi, R., Sangade, J.: Detecting and classifying attacks using artificial neural network. Int. J. Recent Innov. Trends Comput. Commun. 2(5), 1136–1142 (2014)Google Scholar
  11. 11.
    Qiu, C., Shan, J.: Research on intrusion detection algorithm based on BP neural network. Int. J. Secur. Appl. 9(4), 247–258 (2015)Google Scholar
  12. 12.
    Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Springer (2014)Google Scholar
  13. 13.
    Mahmod, S.M., Alnaish, Z.A.H., Al-Hadi, I.A.A.: Hybrid intrusion detection system using artificial bee colony algorithm and multi-layer perceptron. Int. J. Comput. Sci. Inf. Secur (IJCSIS) 13(2) (2015)Google Scholar
  14. 14.
    Abdullah, A., Barati, M., Mahmod, R., Udzir, N.I., Mustapha, N.: Distributed denial of services detection using hybrid machine learning technique. In: IEEE International Symposium on Biometrics and Security Technologies (ISBAST) (2014)Google Scholar
  15. 15.
    Iyengar, N.Ch.S.N., Banerjee, A., Ganapathy, G.: A fuzzy logic based defense mechanism against distributed denial of service attack in cloud computing environment. Int. J. Commun. Netw. Inf. Secur. (IJCNIS), 6(3) (2014)Google Scholar
  16. 16.
    Latif, S., Ashraf, J.: Handling intrusion and DDoS attacks in software defined networks using machine learning techniques. In: IEEE National Software Engineering Conference (2014)Google Scholar
  17. 17.
    Boroujerdi, A.S., Ayat, S.: A robust ensemble of neuro-fuzzy classifiers for DDoS attack detection. In: IEEE International Conference on Computer Science and Network Technology (2013)Google Scholar
  18. 18.
    Kshirsagar, V.K., Tidke, S.M., Vishnu, S.: Intrusion detection system using genetic algorithm and data mining: an overview. Int. J. Comput. Sci. Inform. 1(4) (2012) Google Scholar
  19. 19.
    Odac: A dynamic analog concurrently-processing adaptive chip, http://www.odec.ca/projects/2006/stag6m2/background.html
  20. 20.
  21. 21.
    Itadvicex: Hacker attacks DDOS (distributed denial of service), http://itadvicex.com/hacker-attacks-ddos-distributed-denial-of-service
  22. 22.
    O’Connor, E., Smeaton, A.F., O’Connor, N.E., Regan, F.: A neural network approach to smarter sensor networks for water quality monitoring. Sensors 12(4), 4605–4632 (2012)CrossRefGoogle Scholar
  23. 23.
    Alfantookh, A.A.: DoS attacks intelligent detection using neural networks. J. King Saud Univ. Comput. Inf. Sci. 18, 27–45 (2006)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Uzma Ali
    • 1
  • Kranti K. Dewangan
    • 1
  • Deepak K. Dewangan
    • 1
  1. 1.ITM UniversityNew RaipurIndia

Personalised recommendations