Detecting DDoS Attacks using Machine Learning: Survey
DOI:
https://doi.org/10.29304/jqcsm.2024.16.21548Keywords:
DDoS attack, distributed denial of service, detection, machine learning algorithms(ML), deep learning.Abstract
Phishing attacks have increased dramatically in recent years affecting many areas of society. Phishing attempts often use DDoS attacks to flood a server with too many requests, overwhelming it. DDoS attacks represent a major threat to cybersecurity and pose a significant risk to computer networks. Creating a solid defense system against these attacks is essential but complex due to the wide range of attack methods and complex networks and communication protocols. Ransom demands, revenge, rivalry, or other motives may trigger attacks. This survey discusses DDoS attacks, the advantages and disadvantages of detecting DDoS using machine and deep learning, and a framework for detection using machine learning and deep learning. And use their classifiers to detect DDoS attacks. Furthermore, we explore datasets used in related works. This research is necessary because DDoS attacks are diverse and pose a significant threat to computer networks.
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Raj, R. & Singh Kang, S. Mitigating DDoS Attack using Machine Learning Approach in SDN. Proc. - 2022 4th Int. Conf. Adv. Comput. Commun. Control Networking, ICAC3N 2022 462–467 (2022) doi:10.1109/ICAC3N56670.2022.10074307.
.
Najafimehr, M., Zarifzadeh, S. & Mostafavi, S. A hybrid machine learning approach for detecting unprecedented DDoS attacks. J. Supercomput. 78, 8106–8136 (2022)..
Ashi, Z. Fast and Reliable DDoS Detection using Dimensionality Reduction and Machine Learning. (1959) doi:10.23919/ICITST51030.2020.9351347.
Jyoti, N. & Behal, S. A meta-evaluation of machine learning techniques for detecting DDoS attacks. Proc. 2021 8th Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2021 522–526 (2021) doi:10.1109/INDIACom51348.2021.00093.
Garcia, J. F. C. & Blandon, G. E. T. A Deep Learning-Based Intrusion Detection and Prevention System for Detecting and Preventing Denial-of-Service Attacks. IEEE Access 10, 83043–83060 (2022).
Deepa, V., Sudar, K. M. & Deepalakshmi, P. Design of Ensemble Learning Methods for DDoS Detection in SDN Environment. Proc. - Int. Conf. Vis. Towar. Emerg. Trends Commun. Networking, ViTECoN 2019 1–6 (2019) doi:10.1109/ViTECoN.2019.8899682.
Roempluk, T. & Surinta, O. A machine learning approach for detecting distributed denial of service attacks. ECTI DAMT-NCON 2019 - 4th Int. Conf. Digit. Arts, Media Technol. 2nd ECTI North. Sect. Conf. Electr. Electron. Comput. Telecommun. Eng. 146–149 (2019) doi:10.1109/ECTI-NCON.2019.8692243.
Sambangi, S. & Gondi, L. A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression. 51 (2020) doi:10.3390/proceedings2020063051.
Sallam, A. A., Kabir, M. N., Alginahi, Y. M., Jamal, A. & Esmeel, T. K. IDS for Improving DDoS Attack Recognition Based on Attack Profiles and Network Traffic Features. Proc. - 2020 16th IEEE Int. Colloq. Signal Process. Its Appl. CSPA 2020 255–260 (2020) doi:10.1109/CSPA48992.2020.9068679.
Nandi, S., Phadikar, S. & Majumder, K. Detection of DDoS Attack and Classification Using a Hybrid Approach. ISEA-ISAP 2020 - Proc. 3rd ISEA Int. Conf. Secure. Priv. 2020 41–47 (2020) doi:10.1109/ISEA-ISAP49340.2020.234999.
Ugwu, C. C., Obe, O. O., Popoola, O. S. & Adetunmbi, A. O. A distributed denial of service attack detection system using long-term memory with Singular Value Decomposition. Proc. 2020 IEEE 2nd Int. Conf. Cyberspace, CYBER Niger. 2020 112–118 (2021) doi:10.1109/CYBERNIGERIA51635.2021.9428870.
Pande, S., Khamparia, A., Gupta, D. & Thanh, D. N. H. DDOS Detection Using Machine Learning Technique. tudies in Computational Intelligence vol. 921 (Springer Singapore, 2021).
Mishra, A., Gupta, B. B., Perakovic, D., Penalvo, F. J. G. & Hsu, C. H. Classification Based Machine Learning for Detection of DDoS attack in Cloud Computing. Dig. Tech. Pap. - IEEE Int. Conf. Consum. Electron. 2021-Janua, 2–5 (2021).
Kowsik, A. R. K., Pateriya, R. K. & Verma, P. A Deep Learning-based Hybrid Approach for DDoS Detection in Cloud Computing Environment. 2021 IEEE 4th Int. Conf. Comput. Power Commun. Technol. GUCON 2021 1– 6 (2021) doi:10.1109/GUCON50781.2021.9573817.
Cil, A. E., Yildiz, K. & Buldu, A. Detection of DDoS attacks with feed-forward based deep neural network odel. Expert Syst. Appl. 169, 114520 (2021).
Ismail et al. A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks. IEEE Access 10, 21443–21454 (2022).
Gaur, V. & Kumar, R. Analysis of Machine Learning Classifiers for Early Detection of DDoS Attacks on IoT Devices. Arab. J. Sci. Eng. 47, 1353–1374 (2022).
Chandan, Kumar, S. & Sinha, S. The study by Chandan et al. highlights machine learning methods, including random forest, support vector machine, and nearest neighbors, along with fuzzy inference rules to classify malicious or benign nodes with around 94% accuracy. Proc. 2022 Int. Conf. Intell. Innov. Eng. Technol. ICIIET 2022 294–300 (2022) doi:10.1109/ICIIET55458.2022.9967543.
Kumar, D., Pateriya, R. K., Gupta, R. K., Dehalwar, V. & Sharma, A. DDoS Detection using Deep Learning. Procedia Comput. Sci. 218, 2420–2429 (2022).
Das, S., Dayam, Z. & Chatterjee, P. S. Application of Random Forest Classifier for Prevention and Detection of Distributed Denial of Service Attacks. Proc. - 2022 OITS Int. Conf. Inf. Technol. OCIT 2022 380–384 (2022) doi:10.1109/OCIT56763.2022.00078.
Chavan, N., Kukreja, M., Jagwani, G., Nishad, N. & Deb, N. DDoS Attack Detection and Botnet Prevention using Machine Learning. 8th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2022 1, 1159–1163 (2022).
Nazarudeen, F. & Sundar, S. Efficient DDoS Attack Detection using Machine Learning Techniques. 2022 IEEE. Power Renew. Energy Conf. IPRECON 2022 1–6 (2022) doi:10.1109/IPRECON55716.2022.10059561
.
Kavitha, M. et al. Machine Learning Techniques for Detecting DDoS Attacks in SDN. Int. Conf. Autom. Comput Renew. Syst. ICACRS 2022 - Proc. 634–638 (2022) doi:10.1109/ICACRS55517.2022.10029110.
Qaiser, G., Chandrasekaran, S., Chai, R. & Zheng, J. In the study presented by Ghazia Qaiser et al., I worked to investigate vulnerabilities that negatively affect industrial Internet services. This study evaluated the ability of six machine learning algorithms to detect attacks using the CIC-IDS2017 dataset. T. 2023 15th Int. Conf. Comput. Autom. Eng. ICCAE 2023 546–550 (2023) doi:10.1109/ICCAE56788.2023.10111178.
Mekala, S. & Dasari, K. B. NetBIOS DDoS Attacks Detection with Machine Learning Classification Algorithms. 2023 Int. Conf. Adv. Comput. Comput. Technol. InCACCT 2023 176–179 (2023) doi:10.1109/InCACCT57535.2023.10141815.
Devi, R. S., Bharathi, R. & Kumar, P. K. Investigation on Efficient Machine Learning Algorithm for DDoS Attack Detection. ICCECE 2023 - Int. Conf. Comput. Electr. Commun. Eng.
Ramzan, M. et al. Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm. Sensors (Basel). 23, 1–24 (2023).
Hnamte, V. & Hussain, J. DDoS Detection Using Hybrid Deep Neural Network Approaches. 2023 IEEE 8th Int.Conf. Converg. Technol. I2CT 2023 1–8 (2023) doi:10.1109/I2CT57861.2023.10126434.
Santhosh, S., Sambath, M. & Thangakumar, J. Detection of DDOS Attack using Machine Learning Models. Proc. 1st IEEE Int. Conf. Netw. Commun. 2023, ICNWC 2023 1–6 (2023) doi:10.1109/ICNWC57852.2023.10127537.
Amaad, H. & Mughal, H. Experimenting Ensemble Machine Learning for DDoS Classification: Detection of DDoS Using Large Scale Dataset. 2023 4th Int. Conf. Adv. Comput. Sci. ICACS 2023 - Proc. 1–7 (2023) doi:10.1109/ICACS55311.2023.10089656.
Saeed, M. M. et al. Machine Learning Techniques for Detecting DDOS Attacks. 2023 3rd Int. Conf. Emerg. Smart Technol. Appl. eSmarTA 2023 1–6 (2023) doi:10.1109/eSmarTA59349.2023.10293366.
Fardusy, T., Afrin, S., Sraboni, I. J. & Dey, U. K. An Autoencoder-Based Approach for DDoS Attack Detection Using Semi-Supervised Learning. 2023 Int. Conf. Next-Generation Comput. IoT Mach. Learn. NCIM 2023 1–7 (2023) doi:10.1109/NCIM59001.2023.10212626.
Liu, Z. et al. A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software- Defined Networks. Sensors 23, (2023).
Tavallaee, M., Bagheri, E., Lu, W. & Ghorbani, A. A. A detailed analysis of the KDD CUP 99 data set in Computational Intelligence for Security and Defense Applications. Comput. Intell. Secure. Def. Appl. 1–6 (2009).
dhammad, M., Afdel, K. & Belouch, M. Semi-supervised machine learning approach for DDoS detection. Appl Intell. 48, 3193–3208 (2018).
https://www.unb.ca/cic/datasets/ids-2017.html.
Kanimozhi, V. & Jacob, T. P. Artificial Intelligence outflanks all other machine learning classifiers in Network Intrusion Detection System on the realistic cyber Dataset CSE-CIC-IDS2018 using cloud computing. ICT Express 7, 366–370 (2021).
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Copyright (c) 2024 Sarah Zghair Arrak , Rana Jumma Surayh Al- Janabi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
