Towards a Stochastic Model for Integrated detection and filtering of DoS attacks in Cloud environments

Mir, Iman El; Kim, Dong-Seong; Haqiq, Abdelkrim

doi:10.1145/3090354.3090383

Cited by 4 publications

(1 citation statement)

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“…An attack strategy is designed that does not rely on the knowledge of service rate of the server and baseline user traffic rate. In Reference 18, a mathematical model of a DDoS defense system for the use in clouds is presented. Both the attack flow and the flow of legitimate connection requests are modeled as Poisson processes.…”

Section: The Related Workmentioning

confidence: 99%

On the use of generalized entropy formulas in detection of denial‐of‐service attacks

Basicevic

Blazic²,

Ocovaj

2020

Security and Privacy

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In an earlier journal paper, we have presented the results of a comparison of the performance of five entropy‐based denial‐of‐service detectors (Shannon, Rényi, Tsallis, Bhatia‐Singh, and Ubriaco). The dataset used in that evaluation was generated using simulation. The best performance in terms of detection rate showed Rényi and Bhatia‐Singh detectors. In terms of the detection delay, the best detector was Ubriaco. In this paper, we decided to repeat the evaluation, but this time using a dataset generated using emulation. In this evaluation, the Tsallis entropy‐based detector showed the best performance, followed by the Bhatia‐Singh detector. It can be concluded that both evaluations show the advantage of the detectors based on generalized entropies, compared to the Shannon‐based one, and that Bhatia‐Singh detector in both evaluations performed very well. Another conclusion is that in terms of the detection rate, the flow size distribution outperforms the source port distribution, but in terms of the detection delay, the situation is the opposite.

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Section: The Related Workmentioning

confidence: 99%