Design and Simulation of Lightweight Identity Authentication Mechanism in Body Area Network

Wearable medical devices rely on the human body to form a small LAN around the human body, called body area network (BAN). Users can use these devices to monitor the changes of various body indicators in real time. The physiological data involved in this process belongs to personal privacy. Therefore, the security requirements of BAN are relatively high, and its current research focus is on authentication mechanisms. To meet the requirements of security and resource consumption of BAN, this paper proposes a lightweight identity authentication mechanism that meets the characteristics of BAN resource constraints. Based on the characteristics of BAN, a simple and mature star topology structure is applied to establish the network model of BAN. For the human body in normal situations and emergencies, the corresponding authentication mechanism and encryption and decryption method of physiological data are designed by using the physical unclonable function (PUF) and cloud database, physiological data, and cross-correlation algorithm. Furthermore, the formal and informal security analysis of the designed authentication mechanism proves that the authentication mechanism designed in this paper has certain security, and the lightweight authentication mechanism is simulated and evaluated. The experimental results show that compared with the benchmarking mechanism, the authentication mechanism designed in this paper solves more security problems and has certain advantages in terms of calculation cost, communication cost, and energy cost.

[1]  Thomas Plantard,et al.  Certificate-Based Encryption with Keyword Search: Enabling Secure Authorization in Electronic Health Record , 2016, J. Internet Serv. Inf. Secur..

[2]  Chen Liu,et al.  A mutual auditing framework to protect IoT against hardware Trojans , 2016, 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC).

[3]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[4]  Ngu Nguyen,et al.  Demo of BANDANA - Body Area Network Device-to-device Authentication using Natural gAit , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[5]  Thomas Engel,et al.  A Two-Level Approach to Characterizing Human Activities from Wearable Sensor Data , 2016, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[6]  Antonio F. Gómez-Skarmeta,et al.  Towards Interoperabilty in Identity Federation Systems , 2017, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[7]  Kevin Fu,et al.  Absence Makes the Heart Grow Fonder: New Directions for Implantable Medical Device Security , 2008, HotSec.

[8]  Fan Zhang,et al.  OPFKA: Secure and efficient Ordered-Physiological-Feature-based key agreement for wireless Body Area Networks , 2013, 2013 Proceedings IEEE INFOCOM.

[9]  Otto Schmid,et al.  The implementation of organic principles and values in the European Regulation for organic food , 2009 .

[10]  Roman L. Lysecky,et al.  Security challenges for medical devices , 2015, Commun. ACM.

[11]  Pardeep Kumar,et al.  Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey , 2011, Sensors.

[12]  Jyoteesh Malhotra,et al.  Review on Security Issues and Attacks in Wireless Sensor Networks , 2015 .

[13]  Liping Xie,et al.  Lightweight mutual authentication among sensors in body area networks through Physical Unclonable Functions , 2017, 2017 IEEE International Conference on Communications (ICC).

[14]  Yonghong Chen,et al.  An Efficient Cloud-Assisted Message Authentication Scheme in Wireless Body Area Network , 2017 .

[15]  Thomas Plantard,et al.  Logarithmic size ring signatures without random oracles , 2016, IET Inf. Secur..

[16]  Pardeep Kumar,et al.  E-SAP: Efficient-Strong Authentication Protocol for Healthcare Applications Using Wireless Medical Sensor Networks , 2012, Sensors.

[17]  Xiaofei Wang,et al.  Cloud-enabled wireless body area networks for pervasive healthcare , 2013, IEEE Network.

[18]  Srinivas Sampalli,et al.  Butterfly Encryption Scheme for Resource-Constrained Wireless Networks † , 2015, Sensors.

[19]  Giancarlo Mauri,et al.  Resource-Efficient Hardware Implementation of a Neural-based Node for Automatic Fingerprint Classification , 2017, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[20]  Athanasios V. Vasilakos,et al.  Body Area Networks: A Survey , 2010, Mob. Networks Appl..

[21]  Tony Givargis,et al.  Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks , 2016, Sensors.

[22]  Kevin Fu,et al.  Pacemakers and Implantable Cardiac Defibrillators: Software Radio Attacks and Zero-Power Defenses , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[23]  E Jovanov,et al.  Patient monitoring using personal area networks of wireless intelligent sensors. , 2001, Biomedical sciences instrumentation.

[24]  Young-Sil Lee,et al.  Mutual authentication in wireless body sensor networks (WBSN) based on Physical Unclonable Function (PUF) , 2013, 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC).

[25]  Zhenguo Zhao,et al.  An Efficient Anonymous Authentication Scheme for Wireless Body Area Networks Using Elliptic Curve Cryptosystem , 2014, Journal of Medical Systems.

[26]  Farinaz Koushanfar,et al.  Heart-to-heart (H2H): authentication for implanted medical devices , 2013, CCS.

[27]  Hiroaki Anada,et al.  Decentralized Multi-authority Anonymous Authentication for Global Identities with Non-interactive Proofs , 2019, 2019 IEEE International Conference on Smart Computing (SMARTCOMP).

[28]  Eros Pasero,et al.  Implantable Medical Devices; Networking Security Survey , 2016, J. Internet Serv. Inf. Secur..

[29]  Maged Hamada Ibrahim,et al.  Secure anonymous mutual authentication for star two-tier wireless body area networks , 2016, Comput. Methods Programs Biomed..

[30]  Colleen Swanson,et al.  SoK: Security and Privacy in Implantable Medical Devices and Body Area Networks , 2014, 2014 IEEE Symposium on Security and Privacy.

[31]  Manoj Kumar Security Issues and Privacy Concerns in the Implementation of Wireless Body Area Network , 2014, 2014 International Conference on Information Technology.

[32]  Thomas Plantard,et al.  Device Identification and Personal Data Attestation in Networks , 2018, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[33]  Han-Chieh Chao,et al.  Verifiable, privacy-assured, and accurate signal collection for cloud-assisted wireless sensor networks , 2015, IEEE Communications Magazine.

[34]  Miodrag Potkonjak,et al.  Securing wireless body sensor networks using bijective function-based hardware primitive , 2015, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).