Denial of Service Attacks: Detecting the frailties of machine learning algorithms in the Classication Process



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 classication algorithms with dierent dataset characteristics.


denial of service attacks, intrusion detection systems, classifier performance

Related Project

H2020 ATENA (Advanced Tools to assEss and mitigate the criticality of ICT compoNents and their dependencies over Critical InfrAstructures)


13th International Conference on Critical Information Infrastructures Security (CRITIS 2018), ed. Springer, Kaunas, Lithuania, September 24-26, 2018, Springer series on Security and Cryptology , January 2018

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Year 2020 : 2 citations

 Abiodun Ayodeji, Yong-kuo Liu , "Machine Learning -based process control monitoring and cyber security: Similarities, conflicts and limitations", in Proc. of International Conference on Nuclear Security 2020, Vienna, Austria, February 2020.

 Anthi, E., Williams, L., Rhode, M., Burnap, P., & Wedgbury, A. (2020). Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems. arXiv preprint arXiv:2004.05005.

Year 2019 : 1 citations

 A. Giuseppi, A. Tortorelli, R. Germanà, F. Liberati and A. Fiaschetti, "Securing Cyber-Physical Systems: An Optimization Framework based on OSSTMM and Genetic Algorithms," 2019 27th Mediterranean Conference on Control and Automation (MED), Akko, Israel, 2019, pp. 50-56. DOI: 10.1109/MED.2019.8798506