Medical Data in Wireless Body Area Networks: Device Authentication Techniques and Threat Mitigation Strategies Based on a Token-Based Communication Approach
Abstract
:1. Introduction
1.1. Wireless Body Area Networks
1.2. Meeting Emerging Healthcare Technology Needs
- Data Rate Capabilities: The burgeoning integration of Artificial Intelligence (AI) in healthcare necessitates data rates exceeding the current WBAN standard limit of 10 Mbps. AI-driven applications, particularly those demanding real-time processing and instantaneous decision-making, require bandwidths that can accommodate the increased data throughput.
- Adaptive Security: The landscape of cyber threats is continuously evolving, rendering static security measures insufficient. WBANs must adopt adaptive security protocols that can dynamically respond to new threats, integrating AI to underpin advanced threat detection and adaptive response mechanisms.
- Energy Efficiency: Although the WBAN standard prioritizes low power consumption, there is a pressing need to push beyond the current benchmarks. AI can be pivotal in optimizing power management and tailoring energy usage to user behavior and environmental conditions, thereby extending the wearables’ battery life and operational reliability.
- Encryption and Privacy: With the proliferation of wearables for health monitoring, safeguarding patient data privacy is of the utmost importance. The adoption of advanced encryption techniques and AI-enhanced privacy measures is essential to bolster defenses against unauthorized access and data breaches.
- Interoperability and Personalization: The heterogeneity of devices and systems in the healthcare ecosystem necessitates robust interoperability standards. AI’s potential to personalize device functionality to individual user patterns necessitates the WBAN standard to be flexible enough to accommodate diverse AI models and communication protocols.
2. Communication Framework Description
2.1. Use Case—The Vision
2.2. Overall Communication Mechanism
2.3. Protocol Routine
- Initialization: the master unit broadcasts a signal to wake sensor devices from standby mode, transmitting their data (like MAC address, data type, size, and interrogation or query time) to the master.
- Pathfinding: In this phase, the master calculates the round-trip time and confirms the successful enlistment of the sensor nodes, laying the groundwork for calculating bus schedules. A bus plan or schedule refers to the master’s coordination of which sensor nodes are queried in the next round trip, based on the interrogation or query times previously transmitted by each sensor.
- Communication Routine: This phase involves data transmission throughout the network. The communication routine within the WBAN system begins with a broadcast signal from the master unit. This signal contains a ‘schedule’ or ‘plan’ for the sensor nodes, informing them about their specific roles in the upcoming data transmission cycle. The plan, encoded in a 16-bit long string, dictates whether and when each sensor node will transmit data during the cycle. This strategic scheduling is crucial for maintaining the system’s efficiency and reducing unnecessary data traffic.After the broadcast, the master unit initiates a unicast communication with the first sensor node. The node then attaches its data, along with a sensor-specific header providing details about the time and length of the data packet, to the transmitted structure. If the combined data packet does not exceed the 250-byte limit, it is passed on to the next sensor node. Should the limit be exceeded, the packet is sent back to the master unit for processing, and the communication cycle continues with the next sensor node.
2.4. Human Body Communication
2.5. Security and Data Integrity
3. Security Aspects Regarding Wireless Body Area Framework
3.1. Security of IEEE 802.15.6 Protocols
- Unsecured Communication Level: This is the least-secure level, where data are transmitted through unsecured frames. This lacks mechanisms for data authentication, integrity, confidentiality, and privacy protection.
- Authentication Level: An intermediate security level where data are transmitted through secured authentication without encryption. This level does not support confidentiality or privacy.
- Authentication and Encryption Level: The highest security level involves authenticated and encrypted frame transmission. It addresses security concerns not covered by the previous lower security levels.
3.2. Authentication and Key Agreement
3.2.1. Physical Unclonable Functions
3.2.2. Use of Implicit Certificates
3.2.3. Symmetric Cryptographic-Based Authentication
3.2.4. Biometrics
3.3. Data Transmission
3.3.1. ASCON
3.3.2. Fully Homomorphic Encryption
3.4. Special Consideration
3.4.1. Data Collection
3.4.2. Human–Body Communication as Security Enhancer
3.4.3. Physical Layer Attacks
4. Securing Artificial Intelligence Accompanying the WBAN
4.1. AI on the Sensors within the WBAN
4.2. AI on Edge Environment of WBANs
4.3. AI on a Local Clinic Server
4.4. Collaborative AI between Clinics
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Akyildiz, I.; Guo, H. Holographic-Type Communcation: A New Challenge for the Next Decade. ITU J. Future Evol. Technol. 2022, 3, 421–442. [Google Scholar] [CrossRef]
- WHO. WHO Guideline Recommendations on Digital Interventions for Health System Strengthening; National Library of Medicine, National Center for Biotechnology Information: Bethesda, MD, USA, 2019. Available online: https://www.ncbi.nlm.nih.gov/books/NBK541905/ (accessed on 16 January 2024).
- International Telecommunication Union—Development Sector. Global Connectivity Report 2022. ISBN: 978-92-61-33551-9. Available online: https://www.itu.int/dms_pub/itu-d/opb/ind/d-ind-global.01-2022-pdf-e.pdf (accessed on 16 January 2024).
- Salathè, M.; Wiegand, T.; Wenzel, M. Focus Group on Artificial Intelligence for Health. 2018. Available online: https://arxiv.org/pdf/1809.04797.pdf (accessed on 16 January 2024).
- European Commission. Proposal for a Regulation of the European Parliament and of the Council Laying down Harmonized Rules on Artificial Intelliegence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts; European Commission: Luxembourg, 2021. [Google Scholar]
- IEEE802.15.6; IEEE Standard for Local and Metropolitan Area Networks—Part 15.6: Wireless Body Area Networks. IEEE Computer Society: Washington, DC, USA, 2012; ISBN 9780738172064. [CrossRef]
- Hamalainen, M.; Paso, T.; Mucchi, L.; Girod-Genet, M.; Farserotu, J.; Tanaka, H.; Chin, W.H.; Ismail, L.N. ETSI TC SmartBAN: Overview of the wireless body area network standard. In Proceedings of the 2015 9th International Symposium on Medical Information and Communication Technology (ISMICT), Kamakura, Japan, 24–26 March 2015; pp. 1–5. [Google Scholar] [CrossRef]
- Hamalainen, M.; Mucchi, L.; Girod-Genet, M.; Paso, T.; Farserotu, J.; Tanaka, H.; Anzai, D.; Pierucci, L.; Khan, R.; Alam, M.M.; et al. ETSI SmartBAN Architecture: The Global Vision for Smart Body Area Networks. IEEE Access 2020, 8, 150611–150625. [Google Scholar] [CrossRef]
- Ennesser, F.; Shah, Y. Security Solutions and Services for the IoT, oneM2M IoThing Series. Available online: https://www.onem2m.org/images/images/files/oneM2M_Security_Briefing_A4.pdf (accessed on 16 January 2024).
- Lipps, C.; Tjabben, A.; Rüb, M.; Herbst, J.; Sanon, S.P.; Reddy, R.; Munoz, Y.; Schotten, H.D. Designing Security for the Sixth Generation: About Necessity, Concepts and Opportunities. In Proceedings of the 22nd European Conference on Cyber Warfare and Security (ECCWS2022), Athens, Greece, 22–23 June 2023. [Google Scholar] [CrossRef]
- Toorani, M. On vulnerabilities of the security association in the IEEE 802.15. 6 standard. In Proceedings of the Financial Cryptography and Data Security: FC 2015 International Workshops, BITCOIN, WAHC, and Wearable, San Juan, Puerto Rico, 30 January 2015; Revised Selected Papers. Springer: Berlin/Heidelberg, Germany, 2015; pp. 245–260. [Google Scholar]
- Li, X.; Ibrahim, M.H.; Kumari, S.; Sangaiah, A.K.; Gupta, V.; Choo, K.K.R. Anonymous mutual authentication and key agreement scheme for wearable sensors in wireless body area networks. Comput. Netw. 2017, 129, 429–443. [Google Scholar] [CrossRef]
- Wallrabenstein, J.R. Practical and secure IoT device authentication using physical unclonable functions. In Proceedings of the 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), Vienna, Austria, 22–24 August 2016; pp. 99–106. [Google Scholar]
- Lipps, C.; Weinand, A.; Krummacker, D.; Fischer, C.; Schotten, H.D. Proof of Concept for IoT Device Authentication Based on SRAM PUFs Using ATMEGA 2560-MCU. In Proceedings of the 2018 1st International Conference on Data Intelligence and Security (ICDIS), South Padre Island, TX, USA, 8–10 April 2018; pp. 36–42. [Google Scholar] [CrossRef]
- Nyangaresi, V.O.; Rodrigues, A.J.; Al Rababah, A.A. Secure Protocol for Resource-Constrained IoT Device Authentication. Int. J. Interdiscip. Telecommun. Netw. (IJITN) 2022, 14, 1–15. [Google Scholar] [CrossRef]
- Mucchi, L.; Jayousi, S.; Caputo, S.; Panayirci, E.; Shahabuddin, S.; Bechtold, J.; Morales, I.; Stoica, R.A.; Abreu, G.; Haas, H. Physical-Layer Security in 6G Networks. IEEE Open J. Commun. Soc. 2021, 2, 1901–1914. [Google Scholar] [CrossRef]
- Sciancalepore, S.; Capossele, A.; Piro, G.; Boggia, G.; Bianchi, G. Key management protocol with implicit certificates for IoT systems. In Proceedings of the 2015 Workshop on IoT challenges in Mobile and Industrial Systems, Florence Italy, 18 May 2015; pp. 37–42. [Google Scholar]
- Ha, D.A.; Nguyen, K.T.; Zao, J.K. Efficient authentication of resource-constrained IoT devices based on ECQV implicit certificates and datagram transport layer security protocol. In Proceedings of the 7th Symposium on Information and Communication Technology, Ho Chi Minh, Vietnam, 8–9 December 2016; pp. 173–179. [Google Scholar]
- Khan, H.; Dowling, B.; Martin, K.M. Highly efficient privacy-preserving key agreement for wireless body area networks. In Proceedings of the 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), New York, NY, USA, 1–3 August 2018; pp. 1064–1069. [Google Scholar]
- Sanon, S.P.; Alzalam, I.; Schotten, H.D. Quantum and Post-Quantum Security in Future Networks. In Proceedings of the IEEE Future Networks World Forum 2023, Baltimore, MD, USA, 3–15 November 2023; Available online: https://www.researchgate.net/publication/375665731_Quantum_and_Post-Quantum_Security_in_Future_Networks (accessed on 19 January 2024).
- Chen, A.C. PQCMC: Post-Quantum Cryptography McEliece-Chen Implicit Certificate Scheme. arXiv 2023, arXiv:2401.13691. [Google Scholar]
- Seyhan, K.; Nguyen, T.N.; Akleylek, S.; Cengiz, K. Lattice-based cryptosystems for the security of resource-constrained IoT devices in post-quantum world: A survey. Clust. Comput. 2022, 25, 1729–1748. [Google Scholar] [CrossRef]
- Charjan, S.; Kulkarni, D. Quantum Key Distribution by Exploitation Public Key Cryptography (ECC) in Resource Constrained Devices. Int. J. Emerg. Eng. Res. Technol. 2015, 3, 5–12. [Google Scholar]
- Ometov, A.; Bezzateev, S.; Mäkitalo, N.; Andreev, S.; Mikkonen, T.; Koucheryavy, Y. Multi-Factor Authentication: A Survey. Cryptography 2018, 2, 1. [Google Scholar] [CrossRef]
- Herbst, J.; Petershans, J.; Rüb, M.; Lipps, C.; Beck, A.K.; Carmo, J.C.; Lachmann, T.; Schotten, H.D. Inception Based Deep Learning: Biometric Identification Using Electroencephalography (EEG). In Proceedings of the 2023 International Symposium on Networks, Computers and Communications (ISNCC), Doha, Qatar, 23–26 October 2023; pp. 1–7. [Google Scholar] [CrossRef]
- Herbst, J.; Bergkemper, L.; Petershans, J.; Shobairian, S.; Rüb, M.; Lipps, C. Body Area Networks in the Era of 6G: An Evaluation of Modern Biometrics regarding Multi-Factor-Authentication. In Proceedings of the Workshop on Next Generation Networks and Applications (NGNA 2022), Kaiserslautern, Germany, 8 December 2022. [Google Scholar]
- Cornet, B.; Fang, H.; Ngo, H.; Boyer, E.W.; Wang, H. An Overview of Wireless Body Area Networks for Mobile Health Applications. IEEE Netw. 2022, 36, 76–82. [Google Scholar] [CrossRef]
- Dobraunig, C.; Eichlseder, M.; Mendel, F.; Schläffer, M. Ascon v1.2: Lightweight authenticated encryption and hashing. J. Cryptol. 2021, 34, 33. [Google Scholar] [CrossRef]
- Turan, M.S.; McKay, K.; Chang, D.; Bassham, L.E.; Kang, J.; Waller, N.D.; Kelsey, J.M.; Hong, D. Status Report on the Final Round of the NIST Lightweight Cryptography Standardization Process; NIST Internal Report NIST IR 8454; National Institute of Standards and Technology: Gaithersburg, MD, USA, 2023. [Google Scholar] [CrossRef]
- Gentry, C. Fully homomorphic encryption using ideal lattices. In Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, Bethesda, MD, USA, 31 May–2 June 2009; pp. 169–178. [Google Scholar]
- Private Smart Contracts Using Homomorphic Encryption. Available online: https://www.zama.ai/post/private-smart-contract-using-homomorphic-encryption-ethcc-2022 (accessed on 18 January 2024).
- Mucchi, L.; Jayousi, S.; Martinelli, A.; Caputo, S.; Marcocci, P. An Overview of Security Threats, Solutions and Challenges in WBANs for Healthcare. In Proceedings of the 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT), Oslo, Norway, 8–10 May 2019; pp. 1–6. [Google Scholar] [CrossRef]
- Yin, C.; Zhu, Y.; Fei, J.; He, X. A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks. IEEE Access 2017, 5, 21954–21961. [Google Scholar] [CrossRef]
- Haghighat, M.H.; Li, J. Intrusion detection system using voting-based neural network. Tsinghua Sci. Technol. 2021, 26, 484–495. [Google Scholar] [CrossRef]
- Priya, D.D.; Kiran, A.; Purushotham, P. Lightweight Intrusion Detection System(L-IDS) for the Internet of Things. In Proceedings of the 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), Bhubaneswar, India, 19–20 November 2022; pp. 1–4. [Google Scholar] [CrossRef]
- Roy, S.; Li, J.; Choi, B.J.; Bai, Y. A lightweight supervised intrusion detection mechanism for IoT networks. Future Gener. Comput. Syst. 2022, 127, 276–285. [Google Scholar] [CrossRef]
- Azimjonov, J.; Kim, T. Designing accurate lightweight intrusion detection systems for IoT networks using fine-tuned linear SVM and feature selectors. Comput. Secur. 2024, 137, 103598. [Google Scholar] [CrossRef]
- Alsubaie, F.; Al-Akhras, M.; Alzahrani, H.A. Using Machine Learning for Intrusion Detection System in Wireless Body Area Network. In Proceedings of the 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia, 3–5 November 2020; pp. 100–104. [Google Scholar] [CrossRef]
- European Commission. EU General Data Protection Regulation (GDPR) No. 679/2016; European Commission: Luxembourg, 2016. [Google Scholar]
- McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; Arcas, B.A.y. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Ft. Lauderdale, FL, USA, 20–22 April 2017; Singh, A., Zhu, J., Eds.; PMLR: New York, NY, USA, 2017; Volume 54, pp. 1273–1282. [Google Scholar]
- Albattah, A.; Rassam, M.A. Detection of Adversarial Attacks against the Hybrid Convolutional Long Short-Term Memory Deep Learning Technique for Healthcare Monitoring Applications. Appl. Sci. 2023, 13, 6807. [Google Scholar] [CrossRef]
- Conti, M.; Dragoni, N.; Lesyk, V. A Survey of Man In The Middle Attacks. IEEE Commun. Surv. Tutor. 2016, 18, 2027–2051. [Google Scholar] [CrossRef]
- Awad, M.; Sallabi, F.; Shuaib, K.; Naeem, F. Artificial intelligence-based fault prediction framework for WBAN. J. King Saud Univ.—Comput. Inf. Sci. 2022, 34, 7126–7137. [Google Scholar] [CrossRef]
- Che, L.; Wang, J.; Zhou, Y.; Ma, F. Multimodal Federated Learning: A Survey. Sensors 2023, 23, 6986. [Google Scholar] [CrossRef]
- Hu, H.; Salcic, Z.; Sun, L.; Dobbie, G.; Yu, P.S.; Zhang, X. Membership Inference Attacks on Machine Learning: A Survey. ACM Comput. Surv. 2022, 54, 1–37. [Google Scholar] [CrossRef]
- Truong, N.; Sun, K.; Wang, S.; Guitton, F.; Guo, Y. Privacy preservation in federated learning: An insightful survey from the GDPR perspective. Comput. Secur. 2021, 110, 102402. [Google Scholar] [CrossRef]
- Moshawrab, M.; Adda, M.; Bouzouane, A.; Ibrahim, H.; Raad, A. Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives. Electronics 2023, 12, 2287. [Google Scholar] [CrossRef]
- Kim, J.; Park, G.; Kim, M.; Park, S. Cluster-Based Secure Aggregation for Federated Learning. Electronics 2023, 12, 870. [Google Scholar] [CrossRef]
- Wei, K.; Li, J.; Ding, M.; Ma, C.; Yang, H.H.; Farokhi, F.; Jin, S.; Quek, T.Q.S.; Vincent Poor, H. Federated Learning with Differential Privacy: Algorithms and Performance Analysis. IEEE Trans. Inf. Forensics Secur. 2020, 15, 3454–3469. [Google Scholar] [CrossRef]
- Baruch, G.; Baruch, M.; Goldberg, Y. A Little Is Enough: Circumventing Defenses For Distributed Learning. In Advances in Neural Information Processing Systems; Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R., Eds.; Curran Associates, Inc.: Brooklyn, NY, USA, 2019; Volume 32. [Google Scholar]
- Kim, M.; Günlü, O.; Schaefer, R.F. Federated Learning with Local Differential Privacy: Trade-Offs between Privacy, Utility, and Communication. In Proceedings of the ICASSP 2021—2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 6–12 June 2021; pp. 2650–2654. [Google Scholar] [CrossRef]
- Rüb, M.; Herbst, J.; Lipps, C.; Schotten, H.D. No One Acts like You: AI based Behavioral Biometric Identification. In Proceedings of the 2022 3rd International Conference on Next Generation Computing Applications (NextComp), Flic-en-Flac, Mauritius, 6–8 October 2022; pp. 1–7. [Google Scholar] [CrossRef]
- Qiu, H.; Dong, T.; Zhang, T.; Lu, J.; Memmi, G.; Qiu, M. Adversarial Attacks Against Network Intrusion Detection in IoT Systems. IEEE Internet Things J. 2021, 8, 10327–10335. [Google Scholar] [CrossRef]
Communication Channel | Physical Unclonable Functions | Implicit Certificates | Symmetric Crypto-Based Auth. | Biometrics |
---|---|---|---|---|
HBC Channel | Ideal for device auth.; high security | Limited by proximity | Suitable for key exchange | Effective for user auth. |
Wireless Cyclic Channel | Enhances device fingerprinting; adds complexity | Suitable for trust chains | Optimal for data in transit | Feasible for user auth. |
Edge Device Channel | Useful for device fingerprinting | Critical for secure network interactions | Standard for data protection | User verification before data exit |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Herbst, J.; Rüb, M.; Sanon, S.P.; Lipps, C.; Schotten, H.D. Medical Data in Wireless Body Area Networks: Device Authentication Techniques and Threat Mitigation Strategies Based on a Token-Based Communication Approach. Network 2024, 4, 133-149. https://doi.org/10.3390/network4020007
Herbst J, Rüb M, Sanon SP, Lipps C, Schotten HD. Medical Data in Wireless Body Area Networks: Device Authentication Techniques and Threat Mitigation Strategies Based on a Token-Based Communication Approach. Network. 2024; 4(2):133-149. https://doi.org/10.3390/network4020007
Chicago/Turabian StyleHerbst, Jan, Matthias Rüb, Sogo Pierre Sanon, Christoph Lipps, and Hans D. Schotten. 2024. "Medical Data in Wireless Body Area Networks: Device Authentication Techniques and Threat Mitigation Strategies Based on a Token-Based Communication Approach" Network 4, no. 2: 133-149. https://doi.org/10.3390/network4020007