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Conjoint belief adaption functionality for statistically secure communication paradigm for CR‐IIoTs

In an infrastructure‐critical manufacturing industry, continuous monitoring of the assembly lines with internet‐integrated sensors in an unforeseeable, stochastic, and harsh environment becomes indispensable for real‐time fault detection, location, and isolation. State‐of‐the‐art literature suggest that the Cognitive Radio‐based Industrial Internet of Things (CR‐IIoTs), a spectrum aware wireless communication paradigm, is best suited for monitoring as they are proficient in overcoming spectrum scarcity in a 2.4‐GHz industrial, scientific, and medical (ISM) band and guarantee a threshold Quality of Service (QoS) in hostile environs. Nonetheless, CR‐IIoTs and the Cognitive Radio‐based network archetype are susceptible to internal and external security threats at the application layer and vulnerable to Spectrum Sensing Data Falsification (SSDF) at Data Link Layer (DLL). In CR‐IIoTs, cooperative spectrum sensing (CSS) is a competent technique to ameliorate the performance of Primary User (PU) detection and optimizes the idle licensed spectrum bands access management. In this paper, a novel belief adaption framework in conjunction with an optimized secure communication scheme (termed as CBAF) based on the Certificateless‐Public Key Cryptography (CL‐PKC) is proposed for robust CSS in CR‐IIoTs. CBAF proficiently provides full‐Perfect Forward Secrecy, Ephemeral Secret Leakage‐Resilience, Public Key Replacement‐Resilience Malicious but Passive KGC‐Resilience, and Key Escrow Resilience. Simulation analysis pertaining to network performance indices ascertains the efficacy of CBAF, as it outperforms the state‐of‐the‐art in throughput, packet delivery ratio, and communication overhead by 8.5%, 6.7%, and 19.9% respectively. Under internal attacks, CBAF outperforms the best from state‐of‐the‐art by 11.3% in On‐Off attack, 19.7% in Data Tampering attack, 21.1% in SDF attack, and 13.4% in DDoS attack.