Detecting DDoS Attacks in Cloud Computing Environment

  • Alina Madalina Lonea "Politehnica" University of Timisoara
  • Daniela Elena Popescu University of Oradea
  • Huaglory Tianfield Glasgow Caledonian University


This paper is focused on detecting and analyzing the Distributed Denial of Service (DDoS) attacks in cloud computing environments. This type of attacks is often the source of cloud services disruptions. Our solution is to combine the evidences obtained from Intrusion Detection Systems (IDSs) deployed in the virtual machines (VMs) of the cloud systems with a data fusion methodology in the front-end. Specifically, when the attacks appear, the VM-based IDS will yield alerts, which will be stored into the Mysql database placed within the Cloud Fusion Unit (CFU) of the front-end server. We propose a quantitative solution for analyzing alerts generated by the IDSs, using the Dempster-Shafer theory (DST) operations in 3-valued logic and the fault-tree analysis (FTA) for the mentioned flooding attacks. At the last step, our solution uses the Dempsters combination rule to fuse evidence from multiple independent sources.

Author Biographies

Alina Madalina Lonea, "Politehnica" University of Timisoara
Faculty of Automation and Computers
Daniela Elena Popescu, University of Oradea
Faculty of Electrical Eng. and Information Tech.
Huaglory Tianfield, Glasgow Caledonian University
School of Engineering and Built Environment


[1] Perry, G., Minimizing public cloud disruptions, TechTarget, [online]. Available at:, 2011.

[2] Roschke, S., Cheng, F. and Meinel, C.,Intrusion Detection in the Cloud. In Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, pp. 729-734, 2009.

[3] Yu, D. and Frincke, D.,A Novel Framework for Alert Correlation and Understanding. International Conference on Applied Cryptography and Network Security (ACNS) 2004, Springer's LNCS series, 3089, pp. 452-466, 2004.

[4] Lee, J-H., Park, M-W., Eom, J-H. And Chung, T-M., Multi-level Intrusion Detection System and Log Management in Cloud Computing. In 13th International Conference on Advanced Communication Technology (ICACT) ICACT 2011, Seoul, 13- 16 February, pp.552- 555, 2011.

[5] Chen, Q. and Aickelin, U., Dempster-Shafer for Anomaly Detection. In Proceedings of the International Conference on Data Mining (DMIN 2006), Las Vegas, USA, pp. 232-238, 2006.

[6] Siaterlis, C., Maglaris, B. and Roris, P., A novel approach for a Distributed Denial of Service Detection Engine. National Technical University of Athens. Athens, Greece, 2003.

[7] Siaterlis, C. And Maglaris, B., One step ahead to Multisensor Data Fusion for DDoS Detection. Journal of Computer Security, 13(5):779-806, 2005.

[8] Guth, M.A.S., A Probabilistic Foundation for Vagueness & Imprecision in Fault-Tree Analysis. IEEE Transactions on Reliability, 40(5), pp.563-569, 1991.

[9] Popescu D.E., Lonea A.M., Zmaranda D.,Vancea C. and Tiurbe C., Some Aspects about Vagueness & Imprecision in Computer Network Fault-Tree Analysis. INT J COMPUT COMMUN, ISSN: 1841-9836, 5(4):558-566, 2010.

[10] Esmaili, M., Dempster-Shafer Theory and Network Intrusion Detection Systems. Scientia Iranica, Vol. 3, No. 4, Sharif University of Technology, 1997.

[11] Sentz, K. and Ferson, S., Combination of Evidence in Dempster-Shafer Theory. Sandia National Laboratories, Sandia Report, 2002.

[12] Dissanayake, A., Intrusion Detection Using the Dempster-Shafer Theory. 60-510 Literature Review and Survey, School of Computer Science, University of Windsor, 2008.

[13] Mazzariello, C., Bifulco, R. and Canonico, R., Integrating a Network IDS into an Open Source Cloud Computing Environment. In Sixth International Conference on Information Assurance and Security, pp. 265-270, 2010.

[14] Dhage, S. N., et al., Intrusion Detection System in Cloud Computing Environment. In International Conference and Workshop on Emerging Trends in Technology (ICWET 2011) ' TCET, Mumbai, India, pp. 235-239, 2011.

[15] Lo, C-C., Huang, C-C. And Ku, J., A Cooperative Intrusion Detection System Framework for Cloud Computing Networks. In 39th International Conference on Parallel Processing Workshops, pp.280-284, 2010.

[16] Yu, D. and Frincke, D., Alert Confidence Fusion in Intrusion Detection Systems with Extended Dempster-Shafer Theory. ACM-SE 43: Proceedings of the 43rd ACM Southeast Conference, pp. 142-147, 2005.

[17] Chou, T., Yen, K.K., Luo, J., Network intrusion detection design using feature selection of soft computing paradigms. International Journal of Computational Intelligence, 4(3):102- 105, 2008.

[18] Chatzigiannakis, V., et al., Data fusion algorithms for network anomaly detection: classification and evaluation. Proceedings of the Third International Conference on Networking and Services (ICNS'07), 2007.

[19] Hu, W., Li, J. and Gao, Q., Intrusion Detection Engine Based on Dempster-Shafer's Theory of Evidence. Communications, Circuits and Systems Proceedings, 2006 International Conference, 3:1627-1631, 2006.
How to Cite
LONEA, Alina Madalina; POPESCU, Daniela Elena; TIANFIELD, Huaglory. Detecting DDoS Attacks in Cloud Computing Environment. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 8, n. 1, p. 70-78, nov. 2012. ISSN 1841-9844. Available at: <>. Date accessed: 06 aug. 2020. doi:


cloud computing, cloud security, Distributed Denial of Service (DDoS) attacks, Intrusion Detection Systems, data fusion, Dempster-Shafer theory.