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A Hybrid Optimization Algorithm Based on Ant Colony and Particle Swarm Algorithm to Address IP Traceback Problem

  • Amrita Saini
  • Challa Ramakrishna
  • Sachin Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)

Abstract

The Internet today is highly vulnerable to security threats. The rate of cybercrime has increased proportionately with its usage. Out of numerous possible attacks, the most precarious is Denial of Service (DoS) attack. In DoS the attacker uses the vulnerabilities of compromised hosts in a network and create an attack network called Botnet. The identity of the bots is disguised by using fake source addresses in Internet Protocol (IP) header known as address spoofing. Further, the stateless nature of IP does not allow verification of source address thus making the attack easier. The best way to handle DoS attacks is to reach the source of the attack and block it. IP traceback is a proactive and effective approach to detect the origin of the DoS attack. Once attack origin is detected attack can be blocked, routine network traffic can be restored, chances of future attacks can be prevented and most importantly the responsible attacker can be brought in front of the law. The technique of backtracking for finding an anonymous attacker on a vast network is a complex combinatorial optimization problem, which falls under NP-hard category. In this paper, we have proposed a hybrid approach by integrating Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), to find the efficient solution of IP traceback problem. The main focus of our work is to increase the convergence rate and further reduce the computational complexity of ACO algorithm by combining the distance-based search technique used by ACO with particle velocity based search used by PSO algorithm. The performance of proposed algorithm is evaluated by simulating it on network simulator 2 and the results show that the method can successfully and efficiently detect the DoS attack path with reduced convergence time and computational complexity.

Keywords

DoS TCP/IP ICMP PSO ACO 

References

  1. 1.
    Patel, S., Jha, V.: Various anti IP spoofing techniques. J. Eng. Comput. Appl. Sci. 4(1), 27–31 (2015)Google Scholar
  2. 2.
    Gupta, N., Dhiman, M.: A study of DDOS attacks, tools and DDOS defense mechanisms. Int. J. Eng. Res. Appl. 1(3), 438–440 (2011)Google Scholar
  3. 3.
    Moore, D., Shannon, C., Brown, D., Voelker, G., Savage, S.: Inferring internet denial-of-service activity. ACM Trans. Comput. Syst. 42(2), 115–139 (2006)CrossRefGoogle Scholar
  4. 4.
    Mirkovic, J., Reiher, P.: A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Comput. Commun. Rev. 34(2), 39–53 (2004)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a co-operative learning approach to traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  6. 6.
    Aghaei-Foroushani, V., Heywood, A.: Probabilistic flow marking for IP traceback (PFM). IEEE Trans. Comput., 229–236 (2015)Google Scholar
  7. 7.
    Liu, J., Lee, Z., Chung, Y.: Dynamic Probabilistic packet marking for efficient IP traceback. Elsevier J. Comput. Netw. 51(3), 866–882 (2007)CrossRefGoogle Scholar
  8. 8.
    Yu, S., Zhou, W., Guo, S., Guo, M.: A feasible IP traceback framework through dynamic deterministic packet marking. IEEE Trans. Comput. 65(5), 1418–1427 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Saurabh, S., Sairam, A.: ICMP based IP traceback with negligible overhead for highly distributed reflector attack using bloom filters. Elsevier J. Comput. Commun. 42, 60–69 (2014)CrossRefGoogle Scholar
  10. 10.
    Aljifri, H., Smets, M., Pons, A.: IP traceback using header compression. Elsevier J. Comput. Secur. 22(2), 136–151 (2003)CrossRefGoogle Scholar
  11. 11.
    Fen, Y., Hui, Z., Shuang, C., Xin-Chun, Y.: A lightweight IP traceback scheme depending on TTL. Elsevier J. Procedia Eng. 29, 1932–1937 (2012)Google Scholar
  12. 12.
    Lai, G., Chen, C., Jeng, B., Chao, W.: Ant-based IP traceback. Elsevier J. Exp. Syst. Appl. 34(4), 3071–3080 (2008)CrossRefGoogle Scholar
  13. 13.
    Hamedi-Hamzehkolaie, M., Sanei, R., Chen, C., Tian, X., Nezhad, M.: Bee-based IP traceback. In: IEEE International Conference on Fuzzy Systems and Knowledge Discovery, pp. 968–972 (2014)Google Scholar
  14. 14.
    Wang, P., Lin, H., Wang, T.: An improved ant colony system algorithm for solving IP traceback problem. Elsevier J. Inf. Sci. 326, 172–187 (2016)CrossRefGoogle Scholar
  15. 15.
    Kennedy, J., Eberhart, R.: A new optimizer using particles swarm theory. In: Proceedings of Sixth International Symposium on Micro machine and Human science, IEEE Service Center, Piscataway, pp. 39–43 (1995)Google Scholar
  16. 16.
    Kuo, R., Hong, S., Huang, Y.: Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection. J. Appl. Math Model 34, 3976–3990 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Amrita Saini
    • 1
  • Challa Ramakrishna
    • 1
  • Sachin Kumar
    • 2
  1. 1.National Institute of Technical Teachers Training and ResearchChandigarhIndia
  2. 2.Snow and Avalanche Study Establishment, DRDOChandigarhIndia

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