Systematic Literature Review of Reactive Jamming Attacks Mitigation Techniques in Internet of Things Networks

Internet of Things (IoT) networks have become a prevalently exploited research area in academia and industry. IoT networks benefit from a variety of applications, including smart cities, smart homes, intelligent transportation, smart agriculture, monitoring, surveillance, etc. The security challenges associated with IoT networks have been broadly studied in the literature. This systematic literature review (SLR) is aimed at reviewing the existing research studies on IoT networks’ reactive jamming attacks, challenges, and mitigation. This SLR examined the research studies published between 2019 and 2024 within the popular electronic digital libraries. We selected 45 papers after a rigorous screening of published works to answer the proposed research questions. The outcomes of this SLR reported three major IoT network performance issues. The results showed that the existing mitigation methods are categorized as machine learning based, deception-based, statistical-based, radio frequency-based, game theory-based, and encryption-based. The results show that most methods can detect reactive jamming attacks with accuracy. However, those methods still require additional infrastructure, encryption systems, and lead to prolonged training delays due to large datasets, resulting in computational overhead and transmission delays. Furthermore, the methods are unable to provide a better defense response to reactive jamming attacks. This is because the methods cannot adequately deal with the increased power consumption of IoT devices, cannot minimize transmission delays, and cannot improve the packet delivery ratio. As a result, reactive jamming attacks continue to be prevalent in IoT networks.