Arti Verma, Mohammad Arif, Mohd. Shahid Husain


The cloud environment is a large scale dynamic distributed emerging technology and popularized with the communication, networking, storage theorizes and power. Human being share digital information to new demands which are growing rapidly with respect to time. Cloud is very challenging internet-based computing infrastructure. DDoS (Distributed Denial of Services) is one of the main attack occurred in cloud environment. This leads to financial harms or influences the reputation. Survey statics shows that DDoS attack is rapidly growing attack that targets two major components. In this paper we surveyed different scenarios of DDoS attack. We have focused on different methods classification, detection and defense of DDoS attack, and compared different learning approaches for DDoS. The machine learning is efficiently used for DDoS attack defense.



Cloud Environment, Distributed Computing, Deep Learning, Distributed Denial of Services (DDoS), DoS.

Full Text:



P. K. Rai and R. K. Bunkar, “Study of security risk and vulnerabilities of cloud computing,” Int. J. Comput. Sci. Mobile Comput, vol. 3, pp. 490–496, 2014.

P. Mell and T. Grance, “ The NIST definition of cloud computing,” 2011.

C. Belbergui, N. Elkamoun and R. Hilal, “Cloud Computing: Overview and Risk Identification Based on Classification by Type,” Cloud Computing Technologies and Applications (CloudTech), 3rd International Conference of IEEE Rabat, Morocco, 2017.

B. Khadka, C. Withana, A. Alsadoon, A. Elchouemi “Distributed Denial of Service attack on Cloud: Detection and Prevention,” International Conference and Workshop on Computing and Communication (IEMCON), 2015.

B. Prabadevi, N. Jeyanthi ,”Distributed Denial of service Attacks and its effects on Cloud Environment- a Survey,” The International Symposium on Networks, Computers and Communications, 2014.

Arbor Networks, Inc. (2013). Top Daily DDoS attack worldwide. Retrieved 2018, from Digital Attack Map

Z. Xiao and Y. Xiao, “ Security and privacy in cloud computing,” IEEE Commun. Surveys & Tutorials, vol. 15, no. 2, pp. 843–859, Second Quarter 2013.

D. Linthicum, “As cloud use grows, so will rate of DDoS attacks,” Tech. Rep., Feb. 2013. [Online]. Available:

S. Specht and R. Lee, “ Taxonomies of distributed denial of service networks, attacks, tools and countermeasures,” Technical Report CE-L200303, Princeton University, http://www.princeton. edu/˜ rblee/ELE572 F04Readings. html, Tech. Rep., 2003.

Rashmi V. Deshmukha, Kailas K. Devadkarb “Understanding DDoS Attack & Its Effect In Cloud Environment” e 4th International Conference on Advances in Computing, Communication and Control (ICAC3’15), 2015.

G. Somani, M. S. Gaur, D. Sanghi, M. Conti, R. K. Buyya “DDoS Attacks in Cloud Computing: Issues, Taxonomy, and Future Directions” ACM Computing Surveys, Vol. 1, No. 1, Article 1, December 2015.

Radware, e-center/ddos-chronicles/ddos-attacks-history/


Gary C. Kessler “Defenses Against Distributed Denial of Service Attacks,” 4th edition of the Computer Security Handbook, November 2000

B. Prabadevi, N.Jeyanthi, “Distributed Denial of service Attacks and its effects on Cloud Environment- a Survey,” International Symposium on Networks, Computers and Communications, July 2014

Digital Trends computing/ddos-attacks-hit-record-numbers-in-q2-2015/

S. Weagle, “New Report Points to Alarming DDoS Attack Statistics and Projections,” corero June 27, 2016

Javapipe “JavaPipe > DDoS Protection > Blog > 35 Types of DDoS Attacks Explained,”

Securelist ,Alexander Khalimonenko, Oleg Kupreev “DDOS attacks in Q1 2017,” May 2017

B. Casey, M. Park, Calif. Cyber security ventures 2017””

NETSCOUT “Arbor’s 13th Annual Worldwide Infrastructure Security Report” 2017

. S. T. Zargar, J. Joshi, and D. Tipper, “A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks,” IEEE Commun. Surveys & Tutorials, vol. 15, no. 4, pp. 2046–2069, Fourth Quarter 2013.

“Riorey taxonomy of DDoS attacks, riorey taxonomy rev 2.3 2012,” RioRey Inc., Tech. Rep., 2012. [Online]. Available: http://www.riorey.

J. Mirkovic, P. Reiher “A Taxonomy of DDoS Attack and DDoS Defense Mechanisms” 2004

S. Farahmandian, M. Zamani, A. Akbarabadi, J. M. Zadeh, S. M. Mirhosseini, and S. Farahmandian, “ A survey on methods to defend against DDoS attack in cloud computing,” in Proc. Recent Advances in Knowledge Engineering and System Science, Feb. 2013.

R. Braga, E. Mota, and A. Passito, “Lightweight DDoS flooding attack detection using nox/openflow,” in Proc. 35th IEEE Conf. Local Computer Networks (LCN), 2010, pp. 408–415.

S. Yadav, S. K. Subramanian, “Detection of Application Layer DDoS Attack by Feature Learning Using Stacked Autoencoder,” International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), 2016

K. Lee, J. Kim, K. H. Kwon, Y. Han, S. Kim “DDoS attack detection method using cluster analysis” 2008 pp 1659-1665.

C. Wang, T. Miu, X. Luo§, and J. Wang, ”SkyShield: A Sketch-based Defense System Against Application Layer DDoS Attacks,” IEEE Transactions on Information Forensics and Security, vol. xx, no. xx, xxxx.

Z.Chen, G. Xu, V. Mahalingam, L. Ge, J. Nguyen, W. Yu, C. Lu, “A Cloud Computing Based Network Monitoring and Threat Detection System for Critical Infrastructures,” Volume 3, April 2016, Pages 10-23

X. Yuan, C. Li, X. Li, “DeepDefense: Identifying DDoS Attack via Deep Learning”, IEEE International Conference on Smart Computing (SMARTCOMP), 2017.

H. Luo, Z. Chen, J. Li, and Athanasios V. Vasilakos, “Preventing Distributed Denial-of-Service Flooding Attacks With Dynamic Path Identifiers” IEEE Transactions on Information Forensics and Security, VOL. 12, NO. August 2017

Bi Meng, Wang Andi , Xu Jian, Zhou Fucai, “DDOS Attack Detection System based on Analysis of Users’ Behaviors for Application Layer,” IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 2017.

BahmanRashidi, Carol Fung, and Elisa Bertino, “A Collaborative DDoS Defence Framework Using Network Function Virtualization”, IEEE Transactions on Information Forensics and Security, VOL. 12, NO., October 2017.

F. Luo, Patrick PK Chan, Z. Lin, Z. He, “Improving Robustness of Stacked Auto-Encoder Against Evasion Attack based on Weight Evenness,” Procceding of the International Conference on Wavelet Analysis and Pattern Recognition, Ningbo, China (ICWAPR), 2017.

Vincenzo Matta, Mario Di Mauro, and Maurizio Longo , “DDoS Attacks With Randomized Traffic Innovation: Botnet Identification Challenges and Strategies”, IEEE Transactions on Information Forensics and Security, VOL. 12, NO. August 2017.

Y. Xu and Y. Liu, “ DDoS Attack Detection under SDN Context,” IEEE INFOCOM - The 35th Annual IEEE International Conference on Computer Communications, 2016.

P. K. Bediako, “Long Short-Term Memory Recurrent Neural Network for detecting DDoS flooding attacks within TensorFlow Implementation framework,” 2017.

M. E. Ahmed and H. Kim, “DDoS Attack Mitigation in Internet of Things Using Software Defined Networking,” IEEE Third International Conference on Big Data Computing Service and Applications, 2017.

L. Yang, T. Zhang, J. Song, J. S. Wang, P. Chen ,“Defense of DDoS Attack for Cloud Computing,” IEEE International Conference onComputer Science and Automation Engineering (CSAE), 2012.



  • There are currently no refbacks.

Copyright (c) 2018 International Journal of Advanced Research in Computer Science