TY - GEN
T1 - Intrusion Detection in IoT
T2 - 1st IEEE Secure and Trustworthy Cyberinfrastructure for IoT and Microelectronics, SATC 2025
AU - Gharami, Kanchon
AU - Parrilla, Micah
AU - Bhullar, Harkiran Kaur
AU - Davis, William A.
AU - Moni, Shafika Showkat
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Cybersecurity
KW - IDS Taxonomy
KW - Internet of Things (IoT)
KW - Intrusion Detection Systems (IDS)
KW - IoT Network Attacks
KW - IoT Security
UR - https://www.scopus.com/pages/publications/105017959303
U2 - 10.1109/SATC65530.2025.11137243
DO - 10.1109/SATC65530.2025.11137243
M3 - Conference contribution
AN - SCOPUS:105017959303
T3 - 2025 IEEE 1st Secure and Trustworthy Cyberinfrastructure for IoT and Microelectronics, SATC 2025 - Conference Proceedings
BT - 2025 IEEE 1st Secure and Trustworthy Cyberinfrastructure for IoT and Microelectronics, SATC 2025 - Conference Proceedings
A2 - Amsaad, Fathi
A2 - Abdelgawad, Ahmed
A2 - Hameed, Alaa Ali
A2 - Jamil, Akhtar
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 February 2025 through 27 February 2025
ER -