Abstract
Denial of Service attacks, which have become commonplace on the Information and Communications Technologies domain, constitute a class of threats whose main objective is to degrade or disable a service or functionality on a target. The increasing reliance of Cyber-Physical Systems upon these technologies, together with their progressive interconnection with other infrastructure and/or organizational domains, has contributed to increase their exposure to these attacks, with potentially catastrophic consequences. Despite the potential impact of such attacks, the lack of generality regarding the related works in the attack prevention and detection fields has prevented its application in real-world scenarios. This paper aims at reducing that effect by analyzing the behavior of classification algorithms with different dataset characteristics.
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This work was supported by the ATENA European H2020 Project (H2020-DS-2015-1 Project 700581).
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Frazão, I., Abreu, P.H., Cruz, T., Araújo, H., Simões, P. (2019). Denial of Service Attacks: Detecting the Frailties of Machine Learning Algorithms in the Classification Process. In: Luiijf, E., Žutautaitė, I., Hämmerli, B. (eds) Critical Information Infrastructures Security. CRITIS 2018. Lecture Notes in Computer Science(), vol 11260. Springer, Cham. https://doi.org/10.1007/978-3-030-05849-4_19
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