A Framework for Agent-Based Detection and Prevention of DDoS Attacks in Distributed P2P Networks

  • Gera JaideepEmail author
  • Bhanu Prakash Battula


Peer-to-peer networks have become popular as they are able to deliver useful services. They are distributed in nature and utilize distributed resources to perform intended activities. Especially they are widely used for file sharing. Distributed peer-to-peer networks are vulnerable to attacks. An attack that can disrupt services to all users across the globe is Distributed Denial-of-Service (DDoS) attack. DDoS attacks are considered major threat to distributed P2P networks as they are hard to detect. Many techniques came into existence to defeat DDoS attacks in such networks. However, it is still hard to respond quickly to flooding-based DDoS attacks. The rationale behind this is that DDoS attacks are made by adversaries who make use of large number of attacking machines by means of source address spoofing. In this paper, we proposed an architecture that can detect and defend DDoS attacks. The solution is based on two important parameters. They include Time-To-Live (TTL) value which is in IP header and the distance between source and destination (victim). The proposed methodology takes care of traffic control, agent-based trace back and detection of DDoS attack. The agent-based approach can keep track of details of all nodes. We have made extensive simulations in NS2 to demonstrate the proof of concept. The results revealed that the proposed methodology is capable of detecting and preventing DDoS attacks and thus ensures Quality of Service (QoS) for genuine traffic.

Index Terms

Distributed peer-to-peer networks Security Countermeasures Agent-based approach 


  1. 1.
    Jaideep, Gera, & Bhanu Prakash Battula., (2016). Survey on the present state-of-the-art of P2P networks, their security issues and counter measures. International Journal of Applied Engineering Research, 11(1), 616–620.Google Scholar
  2. 2.
    Zargar, Saman Taghavi, Joshi, James, & Tipper, David. (2013). A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE COMMUNICATIONS SURVEYS & TUTORIALS, p, 1–24.Google Scholar
  3. 3.
    Bhuyan, Monowar H., Kashyap, H. J., Bhattacharyya, D. K., & Kalita, J. K. (2012). Detecting distributed denial of service attacks: Methods, tools and future directions. The Computer Journal, p, 1–20.Google Scholar
  4. 4.
    Chung, Yoo. (2011). Distributed denial of service is a scalability problem. Cognitive Science, p, 1–6.Google Scholar
  5. 5.
    Purohit, R., & Bhargava, D. (2017). An illustration to secured way of data mining using privacy preserving data mining. Journal of Statistics and Management Systems, 20(4), 637–645.CrossRefGoogle Scholar
  6. 6.
    Bhargava, D. (2017). Intelligent agents and autonomous robots. In Detecting and mitigating robotic cyber security risks (pp. 275-283). Hershey: IGI Global.Google Scholar
  7. 7.
    Kumar, N., & Bhargava, D. (2017). A scheme of features fusion for facial expression analysis: A facial action recognition. Journal of Statistics and Management Systems, 20(4), 693–701.CrossRefGoogle Scholar
  8. 8.
    Vyas, S., & Vaishnav, P. (2017). A comparative study of various ETL process and their testing techniques in data warehouse. Journal of Statistics and Management Systems, 20(4), 753–763.CrossRefGoogle Scholar
  9. 9.
    Vyas, V., Saxena, S., & Bhargava, D. (2015). Mind reading by face recognition using security enhancement model. In Proceedings of Fourth International Conference on Soft Computing for Problem Solving (pp. 173–180). New Delhi: Springer.Google Scholar
  10. 10.
    Dhaka, V. S., & Vyas, S. (2014). Analysis of server performance with different techniques of virtual databases. Journal of Emerging Trends in Computing and Information Sciences, 5(10).Google Scholar
  11. 11.
    Dhaka, V. S., & Vyas, S. The use and industrial importance of virtual databases.Google Scholar
  12. 12.
    Kim, M., Lima, L., Zhao, F., Barros, J., Medard, M., Koetter, R., Kalker, T., & Han, K. J. (2009). On counteracting byzantine attacks in network coded peer-to-peer networks. IEEE, pp. 1–26.Google Scholar
  13. 13.
    Zhou, C. V., Leckie, C., & Karunasekera, S. (2010). A survey of coordinated attacks and collaborative intrusion detection (pp. 124–140). Amsterdam: Elsevier.Google Scholar
  14. 14.
    Zeidanloo, H. R., Shooshtari, M. J. Z., Amoli, P. V., Safari, M., & Zamani, M. (2010). A taxonomy of botnet detection techniques (pp 1–5). IEEE.Google Scholar
  15. 15.
    Bhargava, D., & Sinha, M. (2013). Performance analysis of agent based IPSM for windows based operating systems. International Journal of Soft Computing and Engineering (IJSCE).Google Scholar
  16. 16.
    Bhargava, D., & Sinha, M. (2012). Design and implementation of agent based inter process synchronization manager. International Journal of Computers and Applications, 46(21), 17–22.Google Scholar
  17. 17.
    Hwang, K., & Li, D. (2010). Trusted cloud computing with secure resources and data coloring (pp 1–9). IEEE.Google Scholar
  18. 18.
    Sharma, Kalpana, & Ghose, M. K. (2010). Wireless sensor networks: An overview on its security threats. IJCA Special Issue on “Mobile Ad-hoc Networks”, MANETs, 1–4.Google Scholar
  19. 19.
    Locher, Thomas, DavidMysicka, Stefan Schmid, & Wattenhofer, Roger. (2010). Poisoning the Kad network (pp. 195–206). Berlin, Heidelberg: Springer.Google Scholar
  20. 20.
    Zeidanloo, H. R., Manaf, A. B., Vahdani, P., Tabatabaei, F., & Zamani, M. (2010). Botnet detection based on traffic monitoring. In 2010 International Conference on Networking and Information Technology (pp. 1–5).Google Scholar
  21. 21.
    Dewan, Prashant, & Dasgupta, Partha. (2010). P2P reputation management using distributed identities and decentralized recommendation chains. IEEE Transactions on Knowledge and Data Engineering, 22(7), 1–14.CrossRefGoogle Scholar
  22. 22.
    Bhargava, D., & Sinha, M. (2012, May). Performance analysis of agent based IPSM. In 2012 International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 253–258). IEEE.Google Scholar
  23. 23.
    Wasef, A., & Lu, R. (2010). Complementing public key infrastructure to secure vehicular ad hoc networks. IEEE Wireless Communications (pp. 1–7).Google Scholar
  24. 24.
    Jin, Z., Anand, S., & Subbalakshmi, K. P. (2010). Robust spectrum decision protocol against primary user emulation attacks in dynamic spectrum access networks (pp. 1–5). IEEE.Google Scholar
  25. 25.
    Zhang, Chi, Sun, Jinyuan, & Fang, Yuguang. (2010). Privacy and Security for Online Social Networks: Challenges and Opportunities. IEEE Network, p, 1–6.Google Scholar
  26. 26.
    Huang, S. C. -H., MacCallum, D., & Du, D. Z. (2010). Network security (pp. 1–284). New York: Springer.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Acharya Nagarjuna UniversityGunturIndia
  2. 2.Tirumala Engineering CollegeGunturIndia

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