Survey on distributed denial of service attack detection using deep learning: A review

[1] T. Abhiroop, S. Babu and B. Manoj, A machine learning approach for detecting DoS attacks in SDN switches,
Proc. Twenty Fourth Nat. Conf. Commun., 2018, pp. 1–6.
[2] B. Acohido and J. Swartz, ABC News Live Journal New York, https://abcnews.go.com//story?id=8271907&page=1,
(2009).
[3] E. Alese, The curious case of the vanishing & exploding gradient, https://medium.com/learn-love-ai/the-curiouscase-of-the-vanishing-explodinggradient-bf58ec6822eb, 2018.
[4] M.Z. Alom, The history began from alexnet: A comprehensive survey on deep learning approaches, arXiv preprint
arXiv:1803.01164, (2018).
[5] J. Brownlee, Stacked long short-term memory networks, https://machinelearningmastery.com/stacked-long-shortterm-memory-networks, 10 (2017), p. 2019.
[6] C.H.T. Chan, Detection DDoS attacks based on neural-network using apache spark, Int. Conf. Appl. Syst. Innov.,
IEEE, 2016, pp. 1–4.
[7] S.K. Dasari and V. Prasad, A novel and proposed comprehensive methodology using deep convolutional neural
networks for flue cured tobacco leaves classification, Int. J. Inf. Technol. 11 (2019), no. 1, 107–117.
[8] H. D’Cruze, P. Wang, R.O. Sbeit and A. Ray, A software-defined networking (SDN) approach to mitigating DDoS
attacks, Inf. Technol. New Gener. 2018, pp. 141–145.
[9] S. Devaraju and S. Ramakrishnan, Performance analysis of intrusion detection system using various neural
network classifiers, Int. Conf. Recent Trends Inf. Technol., IEEE, 2011, pp. 1033–1038.
[10] C. Douligeris and A. Mitrokotsa, DDoS attacks and defense mechanisms: Classification and state-of-the-art,
Comput. Netw. 44 (2004), no. 5, 643–666.
[11] i2tutorials, Deep dive into bidirectional LSTM, https://www.i2tutorials.com/deep-dive-into-bidirectional-lstm/,
(2019).
[12] G. Jain, M. Sharma and B. Agarwal, Optimizing semantic LSTM for spam detection, Int. J. Inf. Technol. 11
(2019), no. 2, 239–250.
[13] S. Jamali and V. Shaker, Defense against SYN flooding attacks: A particle swarm optimization approach, Comput.
Elect. Eng. 40 (2014), no. 6, 2013–2025.
[14] B. Karan, D. Narayan and P. Hiremath, Detection of DDoS attacks in software defined networks, Proc. 3rd Int.
Conf. Comput. Syst. Inf. Technol. Sustain. Sol. 2018, pp. 265–270.
[15] A. Karim, R.B. Salleh, M. Shiraz, S.A.A. Shah, I. Awan and N.B. Anuar, Botnet detection techniques: Review,
future trends, and issues, J. Zhejiang Univ. Sci. 15 (2014), no. 11, 943–983.
[16] I. Kotenko and A. Ulanov, Agent-based simulation of DDOS attacks and defense mechanisms, Int. J. Comput. 4
(2005), no. 2, 113–123.
[17] F. Lau, S.H. Rubin, M.H. Smith and L. Trajkovic, Distributed denial of service attacks, Smc 2000 Conf. Proc.
IEEE Int. Conf. Syst. Man Cyber. 3 (2000), pp. 2275–2280.
[18] D.E. Levine and G.C. Kessler, Computer security handbook, Chapter 11-Denial of Service Attacks, Computer
Security Handbook, S. Bosworth and M.E. Kabay (eds), John Wiley & Sons, 2002.
[19] S. Lim, J. Ha, H. Kim, Y. Kim and S. Yong, A SDN-oriented DDoS blocking scheme for botnet-based attacks,
Proc. 6th Int. Conf. Ubiquitous Future Netw. (ICUFN), 2014, pp. 63–68.
[20] F.S.D. Lima Filho, F.A. Silveira, A. de Medeiros Brito Junior, G. Vargas-Solar and L.F. Silveira, Smart detection:
An online approach for DoS/DDoS attack detection using machine learning, Secur. Commun. Networks 2019
(2019).[21] S.S. Mohammed, R. Hussain, O. Senko, B. Bimaganbetov, J. Lee, F. Hussain, C.A. Kerrache, E. Barka and
M.Z.A. Bhuiyan, A new machine learning-based collaborative DDoS mitigation mechanism in software-defined
network, 2018 14th Int. Conf. Wireless Mobile Comput. Network. Commun., 2018, pp. 1–8.
[22] M. Nguyen, Illustrated guide to LSTM’s and GRU’s: A step by step explanation, Towards Data Science, 2018.
[23] T. Omrani, A. Dallali, B.C. Rhaimi and J. Fattahi, Fusion of ANN and SVM classifiers for network attack
detection, 2017 18th Int. Conf. Sci. Tech. Automatic Control Comput. Engin. IEEE, 2017, pp. 374–377.
[24] Pranj52, Essentials of deep learning: Introduction to long short term memory, https://www.analyticsvidhya.
com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/, 2017.
[25] K.M. Prasad, A.R. Reddy and K.V. Rao, DoS and DDoS attacks: Defense, detection and traceback mechanisms–A
survey, Global J. Comput. Sci. Technol. 14 (2014), no. 7, 1–19.
[26] K. Pykes, The vanishing/exploding gradient problem in deep neural networks, https://towardsdatascience.
com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11, (2020).
[27] O. Rahman, M.A.G. Quraishi and C.-H. Lung, DDoS attacks detection and mitigation in SDN using machine
learning, Proc. IEEE World Cong. Serv. 2642 (2019), 184–189.
[28] S.A. Riga, Two breaches, two enforcement actions, and a Ddos attack: data security and the rise of the internet
of things, J. Internet Law 20 (2017), no. 9, 3–7.
[29] T. Roempluk and O. Surinta, A machine learning approach for detecting distributed denial of service attacks,
2019 Joint Int. Conf. Digital Arts, Media Technol. ECTI Northern Section Conference on Electrical, Electronics,
Computer and Telecommunications Engineering (ECTI DAMT-NCON), IEEE, 2019, pp. 146–149.
[30] S. Selvin, R. Vinayakumar, E.A. Gopalakrishnan, V.K. Menon and K.P. Soman, Stock price prediction using
LSTM, RNN and CNN- sliding window model, Proc. Int. Conf. Adv. Comput. Commun. Inf. 2017, pp. 1643–
1647.
[31] I. Sharafaldin, A.H. Lashkari, S. Hakak and A.A. Ghorbani, Developing realistic distributed denial of service
(DDoS) attack dataset and taxonomy, Int. Conf. Secur. Technol. IEEE, 2019, pp. 1–8.
[32] T.A. Tang, L. Mhamdi, D. McLernon, S.A.R. Zaidi and M. Ghogho, Deep recurrent neural network for intrusion
detection in SDN-based networks, Proc. 4th IEEE Conf. Network Softwarization Workshops (NetSoft), 2018, pp.
202–206.
[33] T. Wood, Convolutional neural network definition— DeepAI., https://deepai.org/machine-learning-glossary-andterms/convolutional-neural-network, (2020).
[34] S. Yadav and S. Subramanian, Detection of application layer DDoS attack by feature learning using stacked
AutoEncoder, Proc. Int. Conf. Comput. Tech. Inf. Commun. Technol. 2016, pp. 361–366.
[35] S. Yan, Understanding LSTM and its diagrams, https://blog.mlreview.com/understanding-lstm-and-its-diagrams37e2f46f1714, 2016.
[36] J. Ye, X. Cheng, J. Zhu, L. Feng and L. Song, A DDoS attack detection method based on SVM in software defined
network, Secur. Commun. Networks 2018 (2018).
[37] X. Yuan, C. Li and X. Li, DeepDefense: Identifying DDoS attack via deep learning, IEEE Int. Conf. Smart
Comput. (SMARTCOMP), IEEE, 2017, pp. 1–8.
[38] S. Yuanyuan, W. Yongming, G. Lili, M. Zhongsong and J. Shan, The comparison of optimizing SVM by GA and
grid search, 13th IEEE Int. Conf. Electron. Measur. Instrum. IEEE, 2017, pp. 354–360.
[39] C. Zhang, Z. Cai, W. Chen, X. Luo and J. Yin, Flow level detection and filtering of low-rate DDoS, Comput.
Netw. 56 (2012), no. 15, 3417–3431.