Abstract
With the rise of IoT and cloud computing, DDoS attacks have become increasingly harmful. This paper presents a survey of techniques for detecting and preventing DDoS attacks, specifically focusing on Shrew DDoS or low-rate DDoS attacks. We explore the use of machine learning for DDoS detection and prevention and introduce a new potential technique that simplifies the process of detecting and preventing DDoS attacks originating from multiple infected machines, typically known as zombie machines. As a future direction, we discuss a new technique to simplify the detection and prevention of shrew DoS attacks originating from multiple infected machines, commonly known as botnets. The insights presented in this paper will be valuable for researchers and practitioners in cybersecurity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, Yu., Hwang, K.: Collaborative detection and filtering of shrew DDoS attacks using spectral analysis. J. Parallel Distrib. Comput. 66(9), 1137–1151 (2006)
Li, L., Lee, G.: DDoS attack detection and wavelets. Telecommun. Syst. 28, 435–451 (2005)
Toutsop, O., Das, S., Kornegay, K.: Exploring the security issues in home-based IoT devices through denial of service attacks. In: 2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI). IEEE (2021)
Takahashi, Y., Inamura, H., Nakamura, Y.: A low-rate DDoS strategy for unknown bottleneck link characteristics. In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE (2021)
Agrawal, P.K., Gupta, B.B., Jain, S.: SVM based scheme for predicting number of zombies in a DDoS attack. In: 2011 European Intelligence and Security Informatics Conference. IEEE (2011)
Huraj, L., Šimon, M., Horák, T.: Resistance of IoT sensors against DDoS attack in smart home environment. Sensors 20(18), 5298 (2020)
Sinha, M., et al.: Securing an accelerator-rich system from flooding-based denial-of-service attacks. IEEE Trans. Emerg. Top. Comput. 10(2), 855–869 (2021)
Araki, R., Hsu, Y.-F., Matsuoka, M.: Early detection of campus network DDoS attacks using predictive models. In: GLOBECOM 2022–2022 IEEE Global Communications Conference. IEEE (2022)
Ali, J., et al.: A machine learning framework for prevention of software-defined networking controller from DDoS attacks and dimensionality reduction of big data. In: 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE (2020)
Ibrahim, R.F., Al-Haija, Q.A., Ahmad, A.: DDoS attack prevention for internet of thing devices using ethereum blockchain technology. Sensors 22(18), 6806 (2022)
Rani, S.V.J., et al.: Detection of DDoS attacks in D2D communications using machine learning approach. Comput. Commun. 198,32–51 (2023)
Shen, Y., Fei, M., Du, D., Zhang, W., Stanković, S., Rakić, A.: Cyber security against denial of service of attacks on load frequency control of multi-area power systems. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds.) LSMS/ICSEE -2017. CCIS, vol. 763, pp. 439–449. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6364-0_44
Al-Maani, M., et al.: A classifier to detect number of machines performing DoS attack against arduino Oplà device in IoT environment. In: 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet). IEEE (2022)
Rustam, F., et al.: Denial of service attack classification using machine learning with multi-features. Electronics 11(22), 3817 (2022)
Biron, Z.A., Dey, S., Pisu, P.: Real-time detection and estimation of denial of service attack in connected vehicle systems. IEEE Trans. Intelli. Transport. Syst. 19(12), 3893–3902 (2018)
Idhammad, M., Afdel, K., Belouch, M.: Detection system of HTTP DDoS attacks in a cloud environment based on information theoretic entropy and random forest. In: Security and Communication Networks 2018 (2018)
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
Singh, H., Baligodugula, V.V., Amsaad, F. (2024). Shrew Distributed Denial-of-Service (DDoS) Attack in IoT Applications: A Survey. 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 683. Springer, Cham. https://doi.org/10.1007/978-3-031-45878-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-45878-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45877-4
Online ISBN: 978-3-031-45878-1
eBook Packages: Computer ScienceComputer Science (R0)
