Detection of DDoS Attack in Cloud Computing and its Prevention: A Systematic Review

Ali Raza*
Department of Computer Science, Bahria University Karachi Campus, Karachi City, Sindh, Pakistan.
Periodicity:January - June'2022
DOI : https://doi.org/10.26634/jcc.9.1.18542

Abstract

Cloud computing is one of the latest and greatest environments for delivering Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS) in digital communications infrastructure. Cloud computing helps the user remotely access the required service as needed through the Internet. But this technological advancement, due to its remote availability in the cloud, leads to new attacks. One of the biggest threats to cloud infrastructure is Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks are clearly trying to exploit the availability of services for a legitimate user. An attacker gains access to a large number of computers (i.e., botnets) by exploiting the vulnerabilities, and then uses the botnets to initiate an organized attack with a large number of targets. This paper analyses the latest methods for detecting and preventing Distributed Denial of Service (DDoS) attacks. It also provided methods and technologies for preventing, detecting, and responding to DDoS flood attacks.

Keywords

Distributed Denial of Service (DDoS), Flooding Attack, Cloud Computing, Cloud Security.

How to Cite this Article?

Raza, A. (2022). Detection of DDoS Attack in Cloud Computing and its Prevention: A Systematic Review. i-manager’s Journal on Cloud Computing, 9(1), 1-8. https://doi.org/10.26634/jcc.9.1.18542

References

[1]. Agrawal, N., & Tapaswi, S. (2017). A lightweight approach to detect the low/high rate IP spoofed cloud DDoS attacks. In 2017, IEEE 7th International Symposium on Cloud and Service Computing (SC2), 118-123. https://doi.org/10.1109/SC2.2017.25
[2]. Agrawal, N., & Tapaswi, S. (2021). An SDN-assisted defense mechanism for the shrew ddos attack in a cloud computing environment. Journal of Network and Systems Management, 29(2), 1-28. https://doi.org/10.1007/s10922-020-09580-7
[3]. Alhisnawi, M., & Ahmadi, M. (2020). Detecting and mitigating DDoS attack in named data networking. Journal of Network and Systems Management, 28(4), 1343-1365. https://doi.org/10.1007/s10922-020-09539-8
[4]. Alsaeedi, A., Bamasag, O., & Munshi, A. (2020). Real- Time DDoS flood attack monitoring and detection (rt- amd) model for cloud computing. In the 4th International Conference on Future Networks and Distributed Systems (ICFNDS), 1-5. https://doi.org/10.1145/3440749.3442606
[5]. Alzahrani, S., & Hong, L. (2018). Detection of distributed denial of service (DDoS) attacks using artificial intelligence on cloud. In 2018, IEEE World Congress on Services (SERVICES), 35-36. https://doi.org/10.1109/SERVICES.2018.00031
[6]. Chen, J., Yang, Y. T., Hu, K. K., Zheng, H. B., & Wang, Z. (2019). DAD-MCNN: DDoS attack detection via multi channel CNN. In Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 484-488. https://doi.org/10.1145/3318299.3318329
[7]. Corrêa, J. H., Ciarelli, P. M., Ribeiro, M. R. N., & Villaça, R. S. (2021). Ml-based ddos detection and identification using native cloud telemetry macroscopic monitoring. Journal of Network and Systems Management, 29(2), 1-28. https://doi.org/10.1007/s10922-020-09578-1
[8]. Devi, B. K., & Subbulakshmi, T. (2017). DDoS attack detection and mitigation techniques in cloud computing environment. In 2017, International Conference on Intelligent Sustainable Systems (ICISS), 512-517. https://doi.org/10.1109/ISS1.2017.8389464
[9]. Elsayed, M. S., & Azer, M. A. (2018). Detection and countermeasures of ddos attacks in cloud computing. In 2018, Tenth International Conference on Ubiquitous and Future Networks (ICUFN), 708-713. https://doi.org/10.1109/ICUFN.2018.8436989
[10]. Hamdani, F. N., & Siddiqui, F. (2019). Detection of DDOS attacks in cloud computing environment. In 2019, International Conference on Intelligent Computing and Control Systems (ICCS), 83-87. https://doi.org/10.1109/ICCS45141.2019.9065429
[11]. He, Z., Zhang, T., & Lee, R. B. (2017). Machine learning based DDoS attack detection from source side in cloud. In 2017, IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), 114-120. https://doi.org/10.1109/CSCloud.2017.58
[12]. Hezavehi, S. M., & Rahmani, R. (2020). An anomalybased framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments. Cluster Computing, 23(4), 2609-2627. https://doi.org/10.1007/s10586-019-03031-y
[13]. Jiao, J., Ye, B., Zhao, Y., Stones, R. J., Wang, G., Liu, X., Wang, S., & Xie, G. (2017). Detecting TCP-based DDoS attacks in baidu cloud computing data centers. In 2017, IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 256-258. https://doi.org/10.1109/SRDS.2017.37
[14]. Lee, Y. J., Baik, N. K., Kim, C., & Yang, C. N. (2018). Study of detection method for spoofed IP against DDoS attacks. Personal and Ubiquitous Computing, 22(1), 35-44. https://doi.org/10.1007/s00779-017-1097-y
[15]. Madhupriya, G., Shalinie, S. M., & Rajeshwari, A. R. (2018). Detecting DDoS attack in cloud computing using local outlier factors. In 2018, 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 859-863. https://doi.org/10.1109/ICOEI.2018.8553920
[16]. Makkawi, A. M., & Yousif, A. (2021). Machine Learning for Cloud DDoS Attack Detection: A Systematic Review. In 2020, International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 1-9. https://doi.org/10.1109/ICCCEEE49695.2021.9429678
[17]. Mishra, A., Gupta, B. B., Peraković, D., Peñalvo, F. J. G., & Hsu, C. H. (2021). Classification based machine learning for detection of DDoS attack in cloud computing. In 2021, IEEE International Conference on Consumer Electronics (ICCE), 1-4. https://doi.org/10.1109/ICCE50685.2021.9427665
[18]. Mondal, H. S., Hasan, M. T., Hossain, M. B., Rahaman, M. E., & Hasan, R. (2017). Enhancing secure cloud computing environment by Detecting DDoS attack using fuzzy logic. In 2017, 3rd International Conference on Electrical Information and Communication Technology (EICT), 1-4. https://doi.org/10.1109/EICT.2017.8275211
[19]. Narwal, P., Singh, S. N., & Kumar, D. (2017). Gametheory based detection and prevention of DoS attacks on networking node in open stack private cloud. In 2017, International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), 481-486. https://doi.org/10.1109/ICTUS.2017.8286057
[20]. Paharia, B., & Bhushan, K. (2018). DDoS Detection and Mitigation in cloud via FogFiter: a defence mechanism. In 2018, 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1-7. https://doi.org/10.1109/ICCCNT.2018.8493704
[21]. Patil, R., Dudeja, H., Gawade, S., & Modi, C. (2018). Protocol specific multi-threaded network intrusion detection system (PM-NIDS) for DoS/DDoS attack detection in cloud. In 2018, 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1-7. https://doi.org/10.1109/ICCCNT.2018.8494130
[22]. Potluri, S., Mangla, M., Satpathy, S., & Mohanty, S. N. (2020). Detection and prevention mechanisms for DDoS attack in cloud computing environment. In 2020, 11th International Conference On Computing, Communication and Networking Technologies (ICCCNT), 1-6. https://doi.org/10.1109/ICCCNT49239.2020.9225396
[23]. Rengaraju, P., Ramanan, V. R., & Lung, C. H. (2017). Detection and prevention of DoS attacks in softwaredefined cloud networks. In 2017, IEEE Conference on Dependable and Secure Computing, 217-223. https://doi.org/10.1109/DESEC.2017.8073810
[24]. Salemi, H., Rostami, H., Talatian-Azad, S., & Khosravi, M. R. (2021). LEAESN: Predicting DDoS attack in healthcare systems based on lyapunov exponent analysis and echo state neural networks. Multimedia Tools and Applications, 1-22. https://doi.org/10.1007/s11042-020-10179-y
[25]. Sambangi, S., & Gondi, L. (2020). A machine learning approach for DDoS (distributed denial of service) attack detection using multiple linear regression. In Proceedings, (63)1, (pp. 51). https://doi.org/10.3390/proceedings2020063051
[26]. Saxena, R., & Dey, S. (2020). DDoS attack prevention using collaborative approach for cloud computing. Cluster Computing, 23(2), 1329-1344. https://doi.org/10.1007/s10586-019-02994-2
[27]. Soliman, A. K., Salama, C., & Mohamed, H. K. (2018). Detecting DNS reflection amplification DDoS attack originating from the cloud. In 2018, 13th International Conference on Computer Engineering and Systems (ICCES), 145-150. https://doi.org/10.1109/ICCES.2018.8639414
[28]. Sophia, G. A., & Gandhi, M. (2017). Stealthy DDoS detecting mechanism for cloud resilience system. In 2017, International Conference on Information Communication and Embedded Systems (ICICES), 1-5. https://doi.org/10.1109/ICICES.2017.8070740
[29]. Tajane, V., & Sharma, D. (2018). Effective detection and prevention of DDoS in cloud computing environment. In 2018, Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1-5. https://doi.org/10.1109/ICCUBEA.2018.8697346
[30]. Vijayalakshmi, J., & Robin, C. R. (2019). An exponent based error detection mechanism against DXDOS attack for improving the security in cloud. Cluster Computing, 22(2), 3749-3758. https://doi.org/10.1007/s10586-018-2261-5
[31]. Wani, A. R., Rana, Q. P., Saxena, U., & Pandey, N. (2019). Analysis and detection of DDoS attacks on cloud computing environment using machine learning techniques. In 2019, Amity International Conference on Artificial Intelligence (AICAI), 870-875. https://doi.org/10.1109/AICAI.2019.8701238
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