Intrusion Detection in IoT: A Comprehensive Review of Techniques, Challenges, and Future Directions
Kanchon Gharami, Micah Parrilla, Harkiran Kaur Bhullar, William A. Davis, Shafika Showkat Moni · 2025
The Internet of Things (IoT) has rapidly evolved, creating a vast network of interconnected devices that increasingly impact everyday life. However, this widespread adoption has also attracted the attention of cybercriminals, exposing IoT devices to a wide range of malicious attacks. Various IoT Intrusion Detection Systems (IDS) have been developed to combat these threats, utilizing machine learning techniques, algorithms, and customized solutions to effectively address the unique challenges encountered by highly collaborative IoT environments. This paper offers a comprehensive review of the evolving landscape of IoT IDS, exploring various detection techniques, deployment strategies, validation approaches, and key datasets used for developing and training IDS. We also explore common IoT network attack types, propose a detailed IDS taxonomy, and analyze existing datasets for training IDS. Finally, we highlight ongoing research challenges and future directions in securing the IoT ecosystem. The insights offered aim to unify diverse research efforts and provide a holistic view to advance the security of IoT systems.