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The IoT (Internet of Things) encompasses numerous networks and connected devices. One of the primary concerns surrounding IoT, according to researchers and security experts, is the potential risks to privacy and cybersecurity. Deep learning offers significant capabilities for self-adjustment, self-organization, and generalization. Recognizing this, advanced deep learning algorithms are employed in this research to address the privacy and security issues plaguing the IoT landscape. To address these concerns, a novel model called BC-Trans Network is proposed, leveraging the strengths of both Blockchain technology and a transformer component. The transformer plays a vital role in identifying abnormal data, enabling the system to take proactive measures against potential threats. In addition Hash-2 is introduced for the verification of IoT users, adding an extra layer of security to the authentication process. The Blockchain model is utilized to securely store user passwords and details, ensuring a robust and tamper-proof authentication mechanism. To validate the proposed model, a publicly available dataset CSE-CIC-IDS2018 is employed. Pre-processing techniques, including feature selection using the chi-square method, are applied to refine the dataset. The transformer module then classifies the data as normal or abnormal, allowing for accurate identification of potential security breaches. To further safeguard the data and protect the privacy of users, a Fully Homomorphic Encryption (FHE) method is employed. This advanced encryption enables the encryption of categorized normal data, ensuring its confidentiality even during transmission and storage. The study's findings support IoT-cloud server security and privacy by demonstrating the effectiveness of the suggested paradigm in identifying and thwarting network threats. With detection times of 225.3 seconds, an accuracy of 99.25%, a precision of 99.53%, a recall of 99.32%, and an F1 score of 99.59%, the proposed system exhibits impressive performance. Furthermore, as the output numbers increase, the system's metrics improve, suggesting its scalability and flexibility.
Ingle et al. (Wed,) studied this question.