Distributed Denial of Service Attack Detection Using Hyper Calls Analysis in Cloud
Автор: K. Umamaheswari, Nalini Subramanian, Manikandan Subramaniyan
Журнал: International Journal of Computer Network and Information Security @ijcnis
Статья в выпуске: 4 vol.15, 2023 года.
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In the scenario of Distributed Denial of Service (DDoS) attacks are increasing in a significant manner, the attacks should be mitigated in the beginning itself to avoid its devastating consequences for any kind of business. DDoS attack can slow down or completely block online services of business like websites, email or anything that faces internet. The attacks are frequently originating from cloud virtual machines for anonymity and wide network bandwidth. Hyper-Calls Analysis(HCA) enables the tracing of command flow to detect any clues for the occurrence of malicious activity in the system. A DDoS attack detection approach proposed in this paper works in the hypervisor side to perform hyper calls based introspection with machine learning algorithms. The system evaluates system calls in hypervisor for the classification of malicious activities through Support Vector Machine and Stochastic Gradient Descent (SVM & SGD) Algorithms. The attack environment created using XOIC attacker tool and CPU death ping libraries. The system’s performance also evaluated on CICDDOS 2019 dataset. The experimental results reveal that more than 99.6% of accuracy in DDoS detection without degrading performance.
Hyper Calls Analysis, Machine Learning, Support Vector Machine, Stochastic Gradient Descent
Короткий адрес: https://sciup.org/15018634
IDR: 15018634 | DOI: 10.5815/ijcnis.2023.04.06
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