Abstract
The essential advantage of cloud computing is that it flexibly scales to fulfill various needs and it provides the sufficient environment that scales up and downsizes quickly as indicated by the interest, so it needs incredible security from DDoS attack to handle vacation impacts of DDoS attacks. Circulate DoS assaults fall on the classification of basic assaults that bargain the accessibility of the system. These assaults have gotten refined and keep on developing at a quick pace so to identify and to take down these assaults have had become a difficult undertaking. In this project, we have designed a hybrid algorithm which consists of a combination of several machine learning techniques to train a model which can be used to detect and classify the type of DDoS attack with greater accuracy than that of each individual machine learning technique used in the hybrid model.
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References
N. Subramanian, A. Jeyaraj, Recent security challenges in cloud computing. Comput. Electr. Eng. 71, 28–42 (2018)
P. Medeira, J. Grover, M. Khorjiya, A survey on detecting application layer ddos using big data technologies. J. Emerg. Technol. Innov. Res. (JETIR) (2019)
A. Lohachab, B. Karambir, Critical analysis of ddos-an emerging security threat over IoT networks. J. Commun. Inf. Netw. 3(3), 57–78 (2018)
I. Sreeram, V.P.K. Vuppala, Http flood attack detection in application layer using machine learning metrics and bio inspired bat algorithm. Appl. Comput. Inform. 15(1), 59–66 (2019)
S. Toklu, M. Şimşek, Two-layer approach for mixed high-rate and low-rate distributed denial of service (ddos) attack detection and filtering. Arab. J. Sci. Eng. 43(12), 7923–7931 (2018)
A. Praseed, P.S. Thilagam, Ddos attacks at the application layer: challenges and research perspectives for safeguarding web applications. IEEE Commun. Surv. Tutor. 21(1), 661–685 (2018)
R. Swami, M. Dave, V. Ranga, Software-defined networking-based ddos defense mechanisms. ACM Comput. Surv. (CSUR) 52(2), 1–36 (2019)
K. Narayanaswamy, S. Malmskog, A. Sambamoorthy, Systems and methods of per-document encryption of enterprise information stored on a cloud computing service (ccs) (October 30 2018). US Patent 10,114,966
A.R. Wani, Q. Rana, U. Saxena, N. Pandey, Analysis and detection of ddos attacks on cloud computing environment using machine learning techniques, in 2019 Amity International Conference on Artificial Intelligence (AICAI) (IEEE, 2019), pp. 870–875
K.M. Prasad, A.R.M. Reddy, K.V. Rao, Ensemble classifiers with drift detection (ECDD) in traffic flow streams to detect ddos attacks. Wirel. Pers. Commun. 99(4), 1639–1659 (2018)
I. Makhdoom, M. Abolhasan, J. Lipman, R.P. Liu, W. Ni, Anatomy of threats to the internet of things. IEEE Commun. Surv. Tutor. 21(2), 1636–1675 (2018)
S.J. Marck, R.C. Smith, System and method for mitigating distributed denial of service attacks in a cloud environment (February 22 2018). US Patent App. 15/241,920
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Varma, D.A., Ashish, R., Venkata Sai Sandeep, V., Venkatesh, B., Kannadasan, R. (2021). Detection of DDOS Attacks Using Machine Learning Techniques: A Hybrid Approach. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1270. Springer, Singapore. https://doi.org/10.1007/978-981-15-8289-9_42
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DOI: https://doi.org/10.1007/978-981-15-8289-9_42
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