AI-driven intrusion detection and lightweight authentication framework for secure and efficient medical sensor networks

Maintaining the confidentiality and integrity of data in Medical Sensor Networks (MSNs) is an important issue due to limited computational resources and the rising risk of cyber-attacks. Traditional security solutions do not offer real-time security but do use minimal energy. In this work, an AI-driven security system is introduced, which comprises the Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS), the dynamic Trust Management System (TMS), and the lightweight Elliptic Curve Cryptography (ECC)-based authentication protocol to increase the security and reliability of MSNs. With the Umas dataset, a supervised CNN model was trained to predict network anomalies, and TMS dynamically generated trust scores of network devices. The authentication protocol, which is based on ECC, was analyzed and designed formally (with the help of BAN logic and ProVerif) and informally to assess its robustness and efficiency. The proposed framework is 95% accurate, which is better than the known IDS models by 8–10%, and its latency is one-twelfth. Its computational overhead is 1/15 to 1/16 that of the existing models. The lightweight authentication scheme was deemed very useful in withstanding replay, impersonation, man-in-the-middle, and eavesdropping attacks. Nonetheless, it consumed 10% more energy than contemporary state-of-the-art methods. The CNN-TMS-ECC model provides a safe, low-latency, and energy-efficient security solution for medical devices, enabling effective communication and real-time intrusion detection in smart healthcare systems.