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 ex...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12278 |
| _version_ | 1811225723514388480 |
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| author | Sharafi Afsaneh Adabi Sepideh Movaghar Ali Al‐Majeed Salah |
| author_facet | Sharafi Afsaneh Adabi Sepideh Movaghar Ali Al‐Majeed Salah |
| author_sort | Sharafi Afsaneh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T09:12:17Z |
| format | Article |
| id | doaj.art-0c8fb478ad54471eab1fc481537fbd26 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-04-12T09:12:17Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-0c8fb478ad54471eab1fc481537fbd262022-12-22T03:38:56ZengWileyIET Communications1751-86281751-86362021-12-0115192390240610.1049/cmu2.12278A two‐layer attack‐robust protocol for IoT healthcare securitySharafi Afsaneh0Adabi Sepideh1Movaghar Ali2Al‐Majeed Salah3Department of Computer Engineering, North Tehran Branch Islamic Azad University Tehran IranDepartment of Computer Engineering, North Tehran Branch Islamic Azad University Tehran IranDepartment of Computer Engineering Sharif University of Technology Tehran IranHead of School of Computer Science University of Lincoln England UKAbstract 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.https://doi.org/10.1049/cmu2.12278Electrical activity in neurophysiological processesCryptographyBioelectric signalsDigital signal processingData securityMobile, ubiquitous and pervasive computing |
| spellingShingle | Sharafi Afsaneh Adabi Sepideh Movaghar Ali Al‐Majeed Salah A two‐layer attack‐robust protocol for IoT healthcare security IET Communications Electrical activity in neurophysiological processes Cryptography Bioelectric signals Digital signal processing Data security Mobile, ubiquitous and pervasive computing |
| title | A two‐layer attack‐robust protocol for IoT healthcare security |
| title_full | A two‐layer attack‐robust protocol for IoT healthcare security |
| title_fullStr | A two‐layer attack‐robust protocol for IoT healthcare security |
| title_full_unstemmed | A two‐layer attack‐robust protocol for IoT healthcare security |
| title_short | A two‐layer attack‐robust protocol for IoT healthcare security |
| title_sort | two layer attack robust protocol for iot healthcare security |
| topic | Electrical activity in neurophysiological processes Cryptography Bioelectric signals Digital signal processing Data security Mobile, ubiquitous and pervasive computing |
| url | https://doi.org/10.1049/cmu2.12278 |
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