Cloud DDoS Attack Detection Model with Data Fusion & Machine Learning Classifiers
Keywords:Cloud Security, DDoS, Machine Learning, Data Fusion
In the current situation, digital technology is a necessary component of daily life for people. During the Covid-19 pandemic, every profit and non-profit making businesses organizations moved online, which caused an exponential rise in incursions and attacks on the digital platform. The Distributed Denial of Service (DDoS) attack, which may quickly paralyse Internet-based services and applications, is one of the deadly threats to emerge. The attackers regularly update their skill tactics, which allows them to get around the current detection and protection systems. The standard detection systems are ineffective for identifying novel DDoS attacks since the volume of data generated and stored has multiplied. So, the main goal of this work is to employ data fusion applications for secure cloud services and demonstrate the detection of DDoS attacks with the applications of machine learning classifiers that can further be helpful for cloud forensic investigation process. A variety of machine learning models, including decision trees, Navies Bayes, SVM, and KNN are used to detect and classify cloud DDoS attacks. The outcomes of the experiments demonstrated that decision tree is the most feasible and better performer method to classify cloud DDoS attacks.
Garima and S. J. Quraishi, "Machine Learning Approach for Cloud Computing Security," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom pp. 158-163, (2022) doi: 10.1109/ICIEM54221.2022.9853056.
T. Bass, Multi-sensor Data Fusion for Next Generation Distributed Intrusion Detection System, In Proceedings of the IRIS National Symposium on Sensor and Data Fusion, (1999).
Utsav Vora; Jayleena Mahato; Hrishav Dasgupta; Anand Kumar; Swarup Kr Ghosh, "Machine Learning–Based Security in Cloud Database—A Survey," in Machine Learning Techniques and Analytics for Cloud Security, Wiley pp.239-269, (2022) doi: 10.1002/9781119764113.ch12.
Emad Ali, Tariq & Chong, Yung-Wey & Manickam, Selvakumar. Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review. Applied Sciences. 13. 3183. 10.3390/app13053183. (2023)
S. Potluri, M. Mangla, S. Satpathy and S. N. Mohanty, "Detection and Prevention Mechanisms for DDoS Attack in Cloud Computing Environment," 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2020, pp. 1-6, (2020) doi: 10.1109/ICCCNT49239.2020.9225396.
F. Musumeci, A. C. Fidanci, F. Paolucci, F. Cugini, and M. Tornatore, “Machine-Learning-enabled DDoS attacks detection in P4 programmable networks,” Journal of Network and Systems Management, vol. 30, no. 1, pp. 1–27(2022) doi: 10.1007/s10922-021-09633-5.
Z. Liu, L. Qian, and S. Tang, “The prediction of DDoS attack by machine learning,” in Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), pp. 681–686 (2022) doi: 10.1117/12.2628658.
U. Islam et al., “Detection of distributed denial of service (DDoS) attacks in IoT based monitoring system of banking sector using machine learning models,” Sustainability, vol. 14, no. 14, p. 8374 (2022) doi: 10.3390/su14148374
Sumathi S & Rajesh R, Comparative study on TCP SYN flood DDoS attack detection: A machine learning algorithm based approach, WSEAS Trans Syst Control, 16(1) 584–591(2021)
Sudar K M, Beulah M, Deepalakshmi P, Nagaraj P & Chinnasamy P, Detection of Distributed Denial of Service Attacks in SDN using Machine learning techniques, in IEEE Int Conf Comput Commun Informat (ICCCI) 1–5 (2021) doi: 10.1109/ICCCI50826.2021.9402517
G. Lucky, F. Jjunju, and A. Marshall, “A lightweight decision-tree algorithm for detecting DDoS flooding attacks,” in 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C) pp. 382–389, (2020), doi: 10.1109/QRS-C51114.2020.00072.
Saini, P. S., Behal, S., & Bhatia, S “Detection of DDoS Attacks using Machine Learning Algorithms”. 7th International Conference on Computing for Sustainable Global Development (INDIA.Com).pp;16-21. (2020).
Bagyalakshmi C & Samundeeswari E S, DDoS attack classification on cloud environment using machine learning techniques with different feature selection methods, Int J, 9(5) (2020).
Wani, A. R., Rana, Q. P., Saxena, U., & Pandey, N. Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques. 2019 Amity International Conference on Artificial Intelligence (AICAI). (2019) doi:10.1109/aicai.2019.8701238
J. Ye, X. Cheng, J. Zhu, L. Feng, and L. Song, “A DDoS attack detection method based on SVM in software defined network,” Security and Communication Networks, pp. 1–8, 2018, doi: 10.1155/2018/9804061.
Khuphiran P., Leelaprute, P., Uthayopas, P., Ichikawa, K., & Watanakeesuntorn, W. Performance Comparison of Machine Learning Models for DDoS Attacks Detection. 2018 22nd International Computer Science and Engineering Conference (ICSEC) (2018). doi:10.1109/icsec.2018.8712757
N. A. Putri, D. Stiawan, A. Heryanto, T. W. Septian, L. Siregar, and R. Budiarto, “Denial of service attack visualization with clustering using K-means algorithm,” in 2017 International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 177–183, (2017) doi: 10.1109/ICECOS.2017.8167129.
M. Zekri, S. El Kafhali, N. Aboutabit, and Y. Saadi, “DDoS attack detection using machine learning techniques in cloud computing environments,” in 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), pp. 1–7, (2017) doi: 10.1109/CloudTech.2017.8284731.
Kumari, K., Mrunalini, M. Detecting Denial of Service attacks using machine learning algorithms. J Big Data 9, 56 (2022). https://doi.org/10.1186/s40537-022-00616-0
Amrish, R., Bavapriyan, K., Gopinaath, V., Jawahar, A. & Kumar, C. V.. DDoS Detection using Machine Learning Techniques. Journal of IoT in Social, Mobile, Analytics, and Cloud, 4(1), 24-32. (2022) doi:10.36548/jismac.2022.1.003
M NALAYINI, C and Katiravan, Jeevaa, Detection of DDoS Attack Using Machine Learning Algorithms www.jetir.org (ISSN-2349-5162) JETIR July 2022, Volume 9, Issue 7, (2022). Available at SSRN: https://ssrn.com/abstract=4173187
Alduailij, M.; Khan, Q.W.; Tahir, M.; Sardaraz, M.; Alduailij, M.; Malik, F. Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method. Symmetry, 14, 1095. (2022) https://doi.org/10.3390/sym14061095
Sumathi, S ; Rajesh, R ; Karthikeyan, N. DDoS Attack Detection Using Hybrid Machine Learning Based IDS Models. Journal of Scientific & Industrial Research.Vol.81,No.03(2022).http://op.niscair.res.in/index.php/JSIR/article/view/58451
Ashutosh Nath Rimal and Raja Praveen, DDOS Attack Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp185-188 (2020) Available at : http://www.jetir.org/papers/JETIR2006031.pdf
Mahajan, Amit, Ifran Sofi, Vibhakar Mansotra. Machine Learning Techniques used for the Detection and Analysis of Modern Types of DDoS Attacks. International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 06 (2017).
S Satpathy, A Mohapatra, “A data fusion based digital investigation model as an effective forensic tool in the risk assessment and management of cyber security systems”, The 7th international conference on computing, communications and control technologies (2009).
How to Cite
Copyright (c) 2023 Lal Mohan Pattnaik, Pratik Kumar Swain, Suneeta Satpathy, Aditya N. Panda
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.