The goal of this paper is to understand the effectiveness of machine learning (ML) algorithms in combatting IoT-related cyber-attacks, with a focus on Denial of Service (DoS) attacks. This paper also explores the overall vulnerabilities of IoT devices to cyber-attacks, and it investigates other datasets that can be used for IoT cyber-defense analysis, using ML techniques. Finally, this paper presents an evaluation of the CICDoS2019 dataset, using the Logistic Regression (LR) algorithm. With this algorithm, a prediction accuracy of 0.997 was achieved.
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