Abstract
With the increasing reliance on computer networks in our daily lives, the threat of network layer DoS (Denial of Service) attacks has become more prevalent. Attackers use various techniques to disrupt network services and cause loss of data, revenue, and reputation. Recent development in machine learning approaches have shown promise in prevention and detection of such types of attacks by several orders of magnitude. In this paper a thorough overview of machine learning approaches for detecting and preventing network layer DoS attacks is presented. Firstly, the basics of network layer DoS attacks, their classification, and the impact of these attacks is discussed. Then, different machine learning techniques and the ways in which they can be utilized for attack detection and prevention is explored. Additionally, analysis on the strengths and limitations of each approach, and provide a comparative study of the most relevant works in this field is done. Finally, some obstacles in research and potential avenues for future exploration is presented. in the field of machine learning-based defense mechanisms against network layer DoS attacks is discussed. In this paper a detailed summary of the most up-to-date advancements or developments in machine learning-based defense mechanisms against network layer DoS attacks is shown and serve as a reference for one and all who are involved in this field.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tayyab, M., Belaton, B., Anbar, M.: ICMPv6-based DoS and DDoS attacks detection using machine learning techniques, open challenges, and blockchain applicability: a review. IEEE Access 8, 170529–170547 (2020)
Xing, F., Wenye, W.: Understanding dynamic denial of service attacks in mobile ad hoc networks. In: MILCoM 2006–2006 IEEE Military Communications conference. IEEE (2006)
Verma, A., Saha, R., Kumar, N., Kumar, G., et al.: A detailed survey of denial of service for IoT and multimedia systems: past, present and futuristic development. Multimedia Tools Appl. 81(14), 19879–19944 (2022). https://doi.org/10.1007/s11042-021-11859-z
Gebremariam, G.G., Panda, J., Indu, S.: Blockchain-based secure localization against malicious nodes in IoT-based wireless sensor networks using federated learning. Wireless Commun. Mobile Comput. 2023 (2023)
Kukreti, S., et al.: DDoS attack using SYN flooding: a case study. In: 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE (2022)
Patel, L., et al.: Machine learning methods in drug discovery. Molecules 25(22), 5277 (2020)
Subbulakshmi, T., et al.: A unified approach for detection and prevention of DDoS attacks using enhanced support vector machines and filtering mechanisms. ICTACT J. Commun. Technol. 4(2), 737–743 (2013)
Baarzi, A.F.: Efficient service deployment on public cloud: a cost, performance, and security perspective. The Pennsylvania State University (2021)
Allagi, S., Rachh, R., Anami, B.: A robust support vector machine based auto-encoder for DoS attacks identification in computer networks. In: 2021 International Conference on Intelligent Technologies (CONIT). IEEE (2021)
Drucker, H., Donghui, W., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Networks 10(5), 1048–1054 (1999)
Al Duwairi, B., et al.: LogDoS: a novel logging-based DDoS prevention mechanism in path identifier-based information centric networks. Comput. Secur. 99, 102071 (2020)
Ye, J., et al.: A DDoS attack detection method based on SVM in software defined network. Secur. Commun Networks 2018 (2018)
Krishnan, D., Singh, S.: Cost-sensitive bootstrapped weighted random forest for DoS attack detection in wireless sensor networks. In: TENCON 2021–2021 IEEE Region 10 Conference (TENCON). IEEE (2021)
Pande, S., Khamparia, A., Gupta, D.: Feature selection and comparison of classification algorithms for wireless sensor networks. J. Ambient Intell. Humanized Comput. 1–13 (2021). https://doi.org/10.1007/s12652-021-03411-6
Singh, N., Virmani, D.: Computational method to prove efficacy of datasets. J. Inf. Optim. Sci. 42(1), 211–233 (2021)
Nishanth, N., Mujeeb, A.: Modeling and detection of flooding-based denial-of-service attack in wireless ad hoc network using Bayesian inference. IEEE Syst. J. 15(1), 17–26 (2020)
Shrivastava, U., Sharma, N.: Artificial neural network based dual layered predictive model for rare attack detection. In: 2020 International Conference on Computational Performance Evaluation (ComPE). IEEE (2020)
Mariam, W.B.W., Negash, Y.: Performance evaluation of machine learning algorithms for detection of SYN flood attack. In: 2021 IEEE AFRICON. IEEE (2021)
Feng, Q., Yang, K., Ma, M., He, D.: Efficient multi-party EdDSA signature with identifiable aborts and its applications to blockchain. IEEE Trans. Inf. Forensics Secur. 18, 1937–1950 (2023). https://doi.org/10.1109/TIFS.2023.3256710
Gupta, B.B., Joshi, R.C., Misra, M.: Defending against distributed denial of service attacks: issues and challenges. Inf. Secur. J.: Global Perspect. 18(5), 224–247 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Bhatta, N.P., Ghimire, A., Hossain, A.A., Amsaad, F. (2024). Comprehensive Survey of Machine Learning Techniques for Detecting and Preventing Network Layer DoS Attacks. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-031-45882-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-45882-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45881-1
Online ISBN: 978-3-031-45882-8
eBook Packages: Computer ScienceComputer Science (R0)