Mitigation Against Denial-of-Service Flooding and Malformed Packet Attacks

  • Arvind R. Bhagat PatilEmail author
  • Nileshsingh V. ThakurEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)


Denial-of-service attack is really a concern and challenging task to deal with due to the extensive use of advanced technologies by attackers. The denial-of-service flood attacks can consume the network resource such as available bandwidth, CPU, memory, thus affecting the normal functioning of the working network. The arrival of unknown source IP addresses, volume of incoming requests from these IP’s and malformed packets invalid flag settings and other invalid packet conditions are the key parameters for filtering decision. In this paper, these features are explored to mitigate DoS attacks effectively. The performance evaluation of the proposed mechanism is done using qualitative and quantitative parameters.


Denial of service Flood attack Malformed packets Mitigation 


  1. 1.
    Sivabalan S, Radcliffe P (2013) A novel framework to detect and block DDoS attack at the application layer. IEEE TENCON Spring conference Google Scholar
  2. 2.
    Sommer R, Paxson V (2010) Outside the closed world: on using machine learning for network intrusion detection. In: Symposium on security and privacyGoogle Scholar
  3. 3.
    Dou W, Chen Q, Chen J (2012) A confidence-based filtering method for DDoS attack defense in cloud environment. Future Gener Comput Syst 29(7):1838–1850CrossRefGoogle Scholar
  4. 4.
    Rashidi B, Bertino E (2017) A collaborative DDoS defence framework using network function virtualization. IEEE Trans Inf Forensics Secur 12(10):2483–2497CrossRefGoogle Scholar
  5. 5.
    Dridi L, Faten Zhani M (2016) SDN-guard: DoS attacks mitigation in SDN networks. In: 5th IEEE international conference on cloud networking (Cloudnet), pp 212–217Google Scholar
  6. 6.
    Wei L, and Fung C (2015) FlowRanger: a request prioritizing algorithm for controller DoS attacks in software defined networks. In: IEEE international conference on communications (ICC), pp 5254–5259Google Scholar
  7. 7.
    You A, Zulkernine M (2007) A distributed defense framework for flooding based DDoS attacks. A master’s thesis, Queen’s University, Kingston, OntarioGoogle Scholar
  8. 8.
    Arun Raj Kumar P, Selvakumar S (2009) A DDoS threat in collaborative environment—a survey on DDoS attack tools and traceback mechanisms. IEEE international advance computing conference (IACC), pp 1275–1280Google Scholar
  9. 9.
    Compagno A, Conti M, Gasti P, Tsudik G (2013) Poseidon: mitigating interest flooding DDoS attacks in named data networking. In: 38th annual IEEE conference on local computer networksGoogle Scholar
  10. 10.
    Chen C, Chang C (2011) A two-tier coordinated defense scheme against DDoS attacks. In: IEEE international conference on computer science and service system (CSSS)Google Scholar
  11. 11.
    Sanmorino A, Yazid S (2013) DDoS attack detection method and mitigation using pattern of the flow. In: IEEE international conference of information and communication technology (ICoICT), pp 12–16Google Scholar
  12. 12.
    Xie Y, Yu S-Z (2009) A large-scale hidden Semi-Markov Model for anomaly detection on user browsing behaviours. IEEE/ACM Trans Netw 17(1)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Yeshwantrao Chavan College of EngineeringNagpurIndia
  2. 2.Nagpur Institute of TechnologyNagpurIndia

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