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SYN Flood Attack Prevention using Particle Swarm Optimization in Cloud Computing Environment

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Zonayed Ahmed, Maliha Mahbub

Zonayed Ahmed and Maliha Mahbub. SYN Flood Attack Prevention using Particle Swarm Optimization in Cloud Computing Environment. International Journal of Computer Applications 177(1):27-33, November 2017. BibTeX

	author = {Zonayed Ahmed and Maliha Mahbub},
	title = {SYN Flood Attack Prevention using Particle Swarm Optimization in Cloud Computing Environment},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2017},
	volume = {177},
	number = {1},
	month = {Nov},
	year = {2017},
	issn = {0975-8887},
	pages = {27-33},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017915661},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Security issues in Cloud Computing is growing as it continues to offer innovative business model and collaboration capabilities for organizations to boost productivity. There are numerous security issues for cloud computing as it encompasses many technologies including networks, virtualization, resource scheduling, load balancing, concurrency control and memory management. A Cloud infrastructure that comprises the vulnerability of Denial of Service (DoS) attacks denies legitimate users from accessing information or services. A DoS attack can be launched in the transport layer using the very old, but still effective, SYN Flood technique. In a SYN flood attack the attacker sends a flood of TCP SYN requests that gets the server busy without actually completing the three-way handshake procedure used in the setup of TCP sessions. This paper presents a Particle Swarm Optimization (PSO) based approach to enhance the defence mechanism of the system against such attacks. The theoretical analysis and simulations show that the proposed optimization model analyses the situation of under attack server and based on the intensity of the attack situation, it provides the best solution to the server. The server can tune itself dynamically to the optimized solution which increases its chance against SYN Flood attacks.


  1. Yang X, Wetherall D, Anderson T, “A DoS-limiting network architecture”, ACM SIGCOMM Computer Communication Review, 2005.
  2. Jamali S, Shaker G, “PSO-SFDD: Defense against SYN flooding DoS attacks by employing PSO algorithm”, Computers & Mathematics with Applications, 2012.
  3. Habib A, Hefeeda M, Bhargava BK, “Detecting Service Violations and DoS Attacks”, NDSS, 2003.
  4. Carl G, Kesidis G, Brooks RR, Rai S, “Denial-of-service attack-detection techniques”, IEEE Internet computing, 2006.
  5. Zunnurhain K, Vrbsky SV, Hasan R, “FAPA: flooding attack protection architecture in a cloud system”, International Journal of Cloud Computing, 2014.
  6. Bedi, H.S. and Shiva, S. 2012. Securing cloud infrastructure against co-resident DoS attacks using game theoretic defense mechanisms. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics. ACM.
  7. Ristenpart, T., Tromer, E., Shacham, H. and Savage, S. 2009. Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds. In Proceedings of the 16th ACM conference on Computer and communications security. ACM.
  8. Long M, Wu CH, Hung JY, “Denial of service attacks on network-based control systems: impact and mitigation”, IEEE Transactions on Industrial Informatics, 2005.
  9. Wang Y, Lin C, Li QL, Fang Y, “A queueing analysis for the denial of service (DoS) attacks in computer networks”, Computer Networks, 2007.
  10. Mirkovic, J., Prier, G. and Reiher, P. 2002. Attacking DDoS at the source. In Network Protocols, 2002. Proceedings. 10th IEEE International Conference on. IEEE.
  11. Mirkovic J, Prier G, Reiher P, “Source-end DDoS defense”, Network Computing and Applications, 2003.
  12. Gil TM, Poletto M, “MULTOPS: A Data-Structure for Bandwidth Attack Detection”, USENIX Security Symposium, 2001.
  13. Morein, W.G., Stavrou, A., Cook, D.L., Keromytis, A.D., Misra, V. and Rubenstein, D. 2003. Using graphic turing tests to counter automated DDoS attacks against web servers. In Proceedings of the 10th ACM conference on Computer and communications security. ACM.
  14. Schwarzkopf R, Schmidt M, Strack C, Martin S, Freisleben B, “Increasing virtual machine security in cloud environments”, Journal of Cloud Computing: Advances, Systems and Applications, 2012.
  15. Livingston F, “Implementation of Breiman’s random forest machine learning algorithm”, ECE591Q Machine Learning Journal Paper, 2005.
  16. Chonka A, Xiang Y, Zhou W, Bonti A, “Cloud security defence to protect cloud computing against HTTP-DoS and XML-DoS attacks”, Journal of Network and Computer Applications, 2011.
  17. Wang H, Jin C, Shin KG, “Defense against spoofed IP traffic using hop-count filtering”, IEEE/ACM Transactions on Networking (ToN), 2007.
  18. Khan S, Traore I, “Queue-based analysis of DoS attacks”, Information Assurance Workshop, 2005.
  19. Pacini E, Mateos C, García Garino C, “Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments”, CLEI Electronic Journal, 2014.


SYN Flood Attack, DoS attacks, Cloud Computing, PSO Algorithm