Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

Autor: Jun Wu, Cong Wang, Yue Yu, Tiecheng Song, Jing Hu
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: ETRI Journal, Vol 42, Iss 6, Pp 976-986 (2020)
Druh dokumentu: article
ISSN: 1225-6463
DOI: 10.4218/etrij.2019-0388
Popis: Internet of Things (IoT) is considered the future network to support wireless communications. To realize an IoT network, sufficient spectrum should be allocated for the rapidly increasing IoT devices. Through cognitive radio, unlicensed IoT devices exploit cooperative spectrum sensing (CSS) to opportunistically access a licensed spectrum without causing harmful interference to licensed primary users (PUs), thereby effectively improving the spectrum utilization. However, an open access cognitive IoT allows abnormal IoT devices to undermine the CSS process. Herein, we first establish a hard‐combining attack model according to the malicious behavior of falsifying sensing data. Subsequently, we propose a weighted sequential hypothesis test (WSHT) to increase the PU detection accuracy and decrease the sampling number, which comprises the data transmission status‐trust evaluation mechanism, sensing data availability, and sequential hypothesis test. Finally, simulation results show that when various attacks are encountered, the requirements of the WSHT are less than those of the conventional WSHT for a better detection performance.
Databáze: Directory of Open Access Journals