Exploring Intrusion Detection Systems (IDS) in IoT Environments
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Abstract
Introduction; The Internet of Things (IoT) has revolutionized numerous sectors, such as home automation, healthcare, and industrial operations, by enabling interconnected devices to facilitate automation, real-time data analysis, and intelligent decision-making. Despite its transformative potential, the rapid proliferation of IoT has introduced critical cybersecurity challenges due to the heterogeneous and fragmented nature of IoT environments. Objective; IoT networks consist of diverse devices with varying capabilities and protocols, making the implementation of standardized security measures complex. Method; Traditional approaches, including encryption, authentication, and access control, often fall short in addressing evolving cyber threats. Intrusion Detection Systems (IDS) tailored to IoT offer a promising solution, enabling real-time monitoring, anomaly detection, and attack prevention. Result: However, the resource constraints of IoT devices and diverse architectures pose significant design challenges for IDS. Future advancements should focus on lightweight, adaptive IDS models leveraging machine learning, artificial intelligence, and blockchain technologies to enhance security frameworks. Collaboration among researchers, industry, and policymakers is essential to develop scalable solutions, ensuring IoT ecosystems remain secure and efficient in combating cyber threats. Conclusions; This paper reviews IoT security fundamentals, evaluates IDS solutions, and highlights key challenges, offering directions for future research to improve IoT cybersecurity through innovative strategies.