A Survey on DDoS Attack Detection Methods Employing Intelligent Techniques
|© 2020 by IJCTT Journal|
|Year of Publication : 2020|
|Authors : Vinayak P R, Gripsy Paul|
|DOI : 10.14445/22312803/IJCTT-V68I11P113|
How to Cite?
Vinayak P R, Gripsy Paul, "A Survey on DDoS Attack Detection Methods Employing Intelligent Techniques," International Journal of Computer Trends and Technology, vol. 68, no. 11, pp. 83-85, 2020. Crossref, 10.14445/22312803/IJCTT-V68I11P113
The Distributed denial-of-service (DDOS) attacks target network resources to disable the websites or services through overloading the resources with a large amount of traffic. The arrival of intelligent systems such as machine learning-based techniques and deep learning techniques have improved the automatic detection of such attacks. The machine-learning-based techniques such as support vector machine (SVM), decision tree, or Na¨?ve Bayes (NB) can efficiently use for classification. CNN or other techniques can also use for constructing deep neural networks to detect attacks. In this paper, we discuss in detail various DDoS attack detection methods.
 Shi Dong and Mudar Sarem. Ddos attack detection method based on improved knn with the degree of ddos attack in software-defined networks. IEEE Access, 8 (2019) 5039–5048.
 Jesus Arturo P`erez-D`?az, Ismael Amezcua Valdovinos, Kim-Kwang Raymond Choo, and Dakai Zhu. Flexible sdn-based architecture for identifying and mitigating low-rate ddos attacks using machine learning. IEEE Access, 8 (2020) 155859–155872.
 Marwane Zekri, Said El Kafhali, Noureddine Aboutabit, and Youssef Saadi. Ddos attack detection using machine learning techniques in cloud computing environments. In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), IEEE.(2017) 1–7.
 Monika Khandelwal, Deepak Kumar Gupta, and Pradeep Bhale. Dos attack detection technique using a backpropagation neural network. In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE. (2016) 1064–1068.
 Quamar Niyaz, Weiqing Sun, and Ahmad Y Javaid. A deep learning-based ddos detection system in software-defined networking (sdn). arXiv preprint arXiv:1611.07400, (2016).
 Shahzeb Haider, Adnan Akhunzada, Ghufran Ahmed, and Mohsin Raza. Deep learning-based ensemble convolutional neural network solution for distributed denial of service detection in sdns. In 2019 UK/China Emerging Technologies (UCET), IEEE. (2019) 1–4.
 Shan Ali and Yuancheng Li. Learning multilevel auto-encoders for ddos attack detection in the smart grid network. IEEE Access, 7:108647–108659, (2019).
 Tong Anh Tuan, Hoang Viet Long, Raghvendra Kumar, Ishaani Priyadarshini, Nguyen Thi Kim Son, et al. Performance evaluation of botnet ddos attack detection using machine learning. Evolutionary Intelligence, (2019) 1–12.
 Bing Wang, Yao Zheng, Wenjing Lou, and Y Thomas Hou. Ddos attack protection in the era of cloud computing and software-defined networking. Computer Networks, 81 (2015) 308–319.
 Alan Saied, Richard E Overall, and Tomasz Radzik. Detection of known and unknown ddos attacks using artificial neural networks. Neurocomputing, 172 (2016) 385–393.
 Yonghao Gu, Kaiyue Li, Zhenyang Guo, and Yongfei Wang. Semisupervised k-means ddos detection method using a hybrid feature selection algorithm. IEEE Access, 7 (2019) 64351–64365.
 Iman Sharafaldin, Arash Habibi Lashkari, Saqib Hakak, and Ali A Ghorbani. Developing realistically distributed denial of service (ddos) attack dataset and taxonomy. In 2019 International Carnahan Conference on Security Technology (ICCST), 1–8. IEEE. (2019).
 Stephen Specht and Ruby Lee. Taxonomies of distributed denial of service networks, attacks, tools, and countermeasures. CEL2003-03, Princeton University, Princeton, NJ, USA, (2003).
 Heng Zhang, Peng Cheng, Ling Shi, and Jiming Chen. Optimal dos attack scheduling in a wireless networked control system. IEEE Transactions on Control Systems Technology, 24(3) (2015) 843–852.
 Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ?ukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30 (2017) 5998–6008.
 Liang Yao, Chengsheng Mao, and Yuan Luo. Graph convolutional networks for text classification. In Proceedings of the AAAI Conference on Artificial Intelligence, 33(2019) 7370–7377.
 Yoon Kim. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882, 2014.
 Mike Schuster and Kuldip K Paliwal. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, 45(11) (1997) 2673–2681.
 Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, (2014).
 CVE Details. [online]. Available: https://www.cvedetails.com/
 FY Osisanwo, JET Akinsola, O Awodele, JO Hinmikaiye, O Olakanmi, and J Akinjobi. Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3) (2017) 128–138.
attack detection, DDoS, deep learning, machine learning, network security