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The rapid proliferation of internet of things (IoT) devices has ushered in a new era of technological development. However, this growth has also exposed these devices to various cybersecurity risks, including command and control (C&amp;C) attacks. C&amp;C attacks involve unauthorized entities taking control of IoT devices to carry out malicious activities. Traditional cybersecurity measures often fall short in addressing these evolving threats. To enhance IoT security and counter C&amp;C threats, this study explores the potential of supervised learning, a subfield of machine learning. Supervised learning, a method that utilizes past data to train machine learning models capable of independently identifying patterns indicative of C&amp;C threats in real time, offers additional protection to IoT networks. This article delves into the advantages and drawbacks of this approach, considering factors such as the need for well-defined labeled datasets, resource constraints of IoT devices, and ethical considerations surrounding data security.

Using Supervised Learning to Detect Command and Control Attacks in IoT