BLOCK CHAIN-ENABLED DEEP LEARNING FRAMEWORK FOR CYBERSECURITY AND SECURE IOT DATA ANALYTICS
DOI:
https://doi.org/10.48047/rw0z6h06Keywords:
Blockchain, Deep Learning, Cybersecurity, Internet of Things (IoT), Data Integrity, Federated LearningAbstract
The proliferation of Internet of Things (IoT) devices has precipitated an unprecedented expansion of the digital attack surface, rendering traditional cybersecurity paradigms increasingly inadequate. The voluminous, high-velocity data generated by IoT ecosystems necessitates robust, intelligent analytics, yet it simultaneously introduces profound vulnerabilities related to data integrity, privacy, and single points of failure. This paper proposes a novel, integrated framework that synergizes the immutable, decentralized trust architecture of blockchain with the potent predictive capabilities of deep learning (DL) to fortify cybersecurity and secure IoT data analytics. The proposed framework leverages blockchain to establish a tamper-proof ledger for recording IoT data transactions and model parameters, thereby ensuring data provenance and auditability. Concurrently, advanced DL models, trained on this verified data stream, are deployed for real-time anomaly detection, intrusion classification, and predictive threat intelligence. This symbiosis not only enhances the security and reliability of the data fueling the analytical models but also facilitates decentralized, collaborative learning—such as federated learning—where model updates are securely aggregated and recorded on the blockchain to preserve data privacy. Our analysis delineates the architectural components, security threat mitigation mechanisms, and performance benchmarks of this hybrid paradigm, arguing that it represents a critical evolution towards resilient, transparent, and trustworthy intelligent systems for the next-generation IoT landscape.
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