A two‐layer attack‐robust protocol for IoT healthcare security

Abstract

The majority of studies in the field of developing identification and authentication protocols for Internet of Things (IoT) used cryptographic algorithms. Using brain signals is also a relatively new approach in this field. EEG signal-based authentication algorithms typically use feature extraction algorithms that require high processing time. On the other hand, the dynamic nature of the EEG signal makes its use for identification/authentication difficult without relying on feature extraction. This paper presents an EEG-and fingerprint-based two-stage identification-authentication protocol for remote healthcare, which is fast, robust, and multilayer-based. A modified Euclidean distance pattern matching method is proposed to match the EEG signal in the identification stage due to its dynamic nature. The authentication stage is also an optimized method with the Genetic Algorithm (GA), which utilizes a modified Diffie–Hellman algorithm. Due to the vulnerability of the Diffie–Hellman algorithm to different types of attacks, the parameters used for this algorithm are extracted from the fingerprint and the EEG signal of the patient to provide a fast and robust authentication method. The proposed method is evaluated using data from patients with spinal cord injuries. Simulating results demonstrated high identification and authentication accuracy of the proposed method. Furthermore, it is extremely fast and efficient.