Using Supervised Learning to Detect Command and Control Attacks in IoT

Introduction

Internet of Things (IoT), which connects billions of devices ranging from smart household appliances to industrial sensors, has emerged as a paradigmatic technological shift that promises to revolutionize industries and everyday life (Kara, 2022). IoT device proliferation has contributed to unprecedented efficiency and convenience and ushered in a new age of cybersecurity problems. Command and Control (C&C) assaults are one of these dangers that are particularly serious and constantly changing. C&C attacks entail hostile actors taking control of IoT devices without authorization and using that access to carry out numerous destructive actions (Othman, 2023). These assaults may take many forms, such as planning massive botnets for distributed denial-of-service (DDoS) attacks or collecting private information from infected devices. C&C attacks are a focus of IoT security research due to their variety and risk of damage.

Traditional cybersecurity defenses often fall short in the face of C&C criminals' highly developed attack strategies in the IoT environment. This has prompted the investigation of cutting-edge strategies, including machine learning, to improve IoT security. In this situation, supervised learning, machine learning, has shown promise in identifying and thwarting C&C assaults (Cuadra-Sánchez & Aracil, 2015). Leveraging historical data to train machine learning models is the foundation for incorporating supervised learning into IoT security methods. After that, these models can independently recognize patterns and abnormalities suggestive of C&C threats in real-time, adding another layer of security for IoT networks (Atzori et al., 2010). IoT devices' often restricted computational resources, such as little memory and processing power, are one of their distinguishing characteristics (Abuagoub, 2022). Implementing effective security measures is made more difficult by these resource constraints. In contexts with limited resources, it may be difficult for conventional intrusion detection systems to function well, which makes machine learning—with its capacity to utilize data effectively—an appealing option.

There are two stages to the supervised learning process. First, a model is trained using examples of known C&C attacks and typical device behavior from a labeled dataset. The model learns about the distinguishing traits of C&C assaults at this phase. The model is then deployed in a real IoT context to observe device behavior once trained continually. A predetermined warning or reaction is started when the model notices behavior that resembles a C&C attack to lessen the hazard.

Although the combination of supervised learning with IoT security offers an appealing path, it is important to understand both the benefits and constraints of this strategy. The benefits include better threat detection accuracy, reduced likelihood of false positives, and flexibility of machine learning models to change attack techniques. There are obstacles to overcome, such as the need for solid labeled datasets, resource limitations on IoT devices, and ethical issues related to data protection (Ahsan et al., 2022). To identify C&C threats in IoT, this survey study article attempts to review the state of the art in this field thoroughly. The study aims to contribute significantly to the expanding body of knowledge in IoT security by analyzing lessons learned from earlier research and weighing the advantages and disadvantages of current works. Researchers, practitioners, and policymakers working in safeguarding IoT ecosystems are among its target audience members. This will help to create safer and more robust IoT environments for all stakeholders (Cioffi et al., 2020).