TY  - JOUR
AU  - Haziq, Muhammad
PY  - 2026
DA  - 2026/10/21
TI  - Recent Developments in Intrusion Detection for the Internet of Underwater Things: A Systematic Review
JO  - Recent Progress in Science and Engineering
SP  - 022
VL  - 02
IS  - 04
AB  - The Internet of Underwater Things (IoUT) has significantly improved underwater communication. However, due to the unique and challenging characteristics of underwater acoustic channels, such as long multipaths, high propagation delay and Doppler variation, and severe energy and bandwidth limitation, IoUT networks are highly vulnerable to security threats. These conditions make conventional Internet of Things (IoT) intrusion detection systems (IDS) less effective. Improving the security of IoUT networks requires specialised intrusion detection approaches tailored to address these challenges. This paper reviews intrusion detection in IoUT. It describes the architecture of IoUT networks, and demonstrates how the channel dynamics affect the capabilities of an IDS. It also categorises threats across the physical, data link, network, transport, and application layers, together with cross-layer attacks, insider threats, and advanced persistent threats. Furthermore, the review compares anomaly-based, trust-based, and hybrid detection frameworks, with emphasis on machine learning, federated learning, blockchain-assisted security, and density-based clustering. The results are compared based on evaluation metrics, latency, energy efficiency, and computational complexity. Finally, the paper outlines open challenges and future research directions, focusing on cross-layer detection, communication-efficient federated learning, adaptive thresholding, dataset availability, and validation at sea.
SN  - 3067-4573
UR  - https://doi.org/10.21926/rpse.2604022
DO  - 10.21926/rpse.2604022
ID  - Haziq2026
ER  -