Securing IoT Networks in Healthcare for Enhanced Privacy in Wearable Patient Monitoring Devices

The project aims to make smart patient monitoring devices more private. This is crucial to healthcare IoT network security. Strong security is needed to protect patient data in a growing digital healthcare system. Our research develops a whole security system with sophisticated encryption, user-centric identity, blockchain integration, and deep learning to detect unusual activity. As mentioned before, sophisticated patient monitoring gadgets in contemporary healthcare have increased security vulnerabilities. A powerful and adaptable security system is needed to secure patient data; hence, a multidimensional approach is studied. This study’s approaches include experimental design, dataset preparation, and measure choice. The security framework uses cutting-edge cryptography, including ECC, homomorphic encryption, and blockchain. Also crucial are recognizing users based on biological features and spotting abnormalities using deep learning and LSTM networks. Mixed methods mitigate all security issues. The findings reveal that the recommended strategy boosts encryption to 256 bits. Since authentication accuracy has reached 98%, user-centered authentication approaches work. Cryptographic processes employ resources effectively, and anomaly detection rates reflect how successfully the approach detects unusual device behavior. Elliptic Curve Cryptography (ECC), which may give 256-bit encryption, is stronger than symmetric encryption (128 bits). User-centric authentication was 98% accurate, outperforming public key infrastructure (95%). With a 92% success rate, LSTM networks can predict abnormal activity in connected devices. The proposed approach performs cryptographic operations fast, with 120 operations per unit time. In conclusion, this research introduces a healthcare IoT security solution. The combined encryption, identity, blockchain, and deep learning technology is secure. The recommended solution protects patient data on personal tracking devices with greater encryption, usercentered identification, and proactive anomaly detection. This study increases research on IoT in healthcare and informs healthcare organizations on how to strengthen privacy rights.