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