Data Security Through Zero-Knowledge Proof and Statistical Fingerprinting in Vehicle-to-Healthcare Everything (V2HX) Communications

The security and privacy of healthcare enterprises (HEs) are crucial because they maintain sensitive information. Because of the unique functional requirement of omni-inclusiveness, HEs are expected to monitor patients, allowing for connectivity with vehicular ad hoc networks (VANETs). In the absence of literature on security provisioning frameworks that connect VANETs and HEs, this paper presents a smart zero-knowledge proof and statistical fingerprinting-based trusted secure communication framework for a fog computing environment. A zero-knowledge proof is used for vehicle authentication, and statistical fingerprinting is employed to secure communication between VANETs and HEs. Authenticity verification of the operations is performed at the on-board unit (OBU) fitted in the vehicle based on the service executions at the resident hardware platform. The processor clock cycles are acquired from the service executions in a complete sandboxed environment. The calculated cycles assist in developing the blueprint signature for the particular OBU of the vehicle. Hence, the fingerprint signature helps build trust and plays a key role in authenticating the vehicle’s horizontal movement to everything or to different sections of the HEs. In an environment enabled for fog computing, our novel model can provide efficient remote monitoring.

[1]  Ali A. Ghorbani,et al.  A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT , 2017, IEEE Access.

[2]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[3]  Athanasios V. Vasilakos,et al.  Secure Data Sharing and Searching at the Edge of Cloud-Assisted Internet of Things , 2017, IEEE Cloud Computing.

[4]  Feng Hao,et al.  Anonymous voting by two-round public discussion , 2010, IET Inf. Secur..

[5]  Feng Hao,et al.  A Fair and Robust Voting System by Broadcast , 2012, Electronic Voting.

[6]  Iftikhar Ahmad,et al.  Towards Improving the Intrusion Detection through ELM (Extreme Learning Machine) , 2020, Computers, Materials & Continua.

[7]  Sangjin Kim,et al.  Rethinking Vehicular Communications: Merging VANET with cloud computing , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[8]  Amjad Gawanmeh,et al.  Taxonomy Analysis of Security Aspects in Cyber Physical Systems Applications , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[9]  Mohamed-Slim Alouini,et al.  Front-end intelligence for large-scale application-oriented internet-of-things , 2016, IEEE Access.

[10]  Azzam Mourad,et al.  A Novel Ad-Hoc Mobile Edge Cloud Offering Security Services Through Intelligent Resource-Aware Offloading , 2019, IEEE Transactions on Network and Service Management.

[11]  Hongtao Song,et al.  Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks , 2020, Int. J. Distributed Sens. Networks.

[12]  Joan Feigenbaum,et al.  Managing trust in an information-labeling system , 1997, Eur. Trans. Telecommun..

[13]  Joel J. P. C. Rodrigues,et al.  Towards energy-aware fog-enabled cloud of things for healthcare , 2018, Comput. Electr. Eng..

[14]  Lewis Tseng,et al.  Reliable Broadcast in Networks with Trusted Nodes , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[15]  Mehmet A. Orgun,et al.  Survey on cybersecurity issues in wireless mesh networks based eHealthcare , 2016, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).

[16]  Joel J. P. C. Rodrigues,et al.  On resilience of Wireless Mesh routing protocol against DoS attacks in IoT-based ambient assisted living applications , 2015, 2015 17th International Conference on E-health Networking, Application & Services (HealthCom).

[17]  Iftikhar Ahmad,et al.  Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000 , 2018 .

[18]  Joe Weinman,et al.  The Economics of the Hybrid Multicloud Fog , 2017, IEEE Cloud Computing.

[19]  Rajkumar Buyya,et al.  Mobility-Aware Application Scheduling in Fog Computing , 2017, IEEE Cloud Computing.

[20]  Javier Gozalvez,et al.  LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications , 2017, IEEE Vehicular Technology Magazine.

[21]  Nathaniel Hamming,et al.  IEEE PHD Cybersecurity Standards Roadmap , 2019 .

[22]  Jon C. Haass,et al.  POStCODE Middleware for Post-Market Surveillance of Medical Devices for Cyber Security in Medical and Healthcare Sector in Australia , 2018, 2018 12th International Symposium on Medical Information and Communication Technology (ISMICT).

[23]  Athanasios V. Vasilakos,et al.  The Future of Healthcare Internet of Things: A Survey of Emerging Technologies , 2020, IEEE Communications Surveys & Tutorials.

[24]  Wanlei Zhou,et al.  FogRoute: DTN-Based Data Dissemination Model in Fog Computing , 2017, IEEE Internet of Things Journal.

[25]  Amjad Gawanmeh An axiomatic model for formal specification requirements of ubiquitous healthcare systems , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[26]  Ali Selamat,et al.  AZSPM: Autonomic Zero-Knowledge Security Provisioning Model for Medical Control Systems in Fog Computing Environments , 2017, 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops).

[27]  Paschalis C. Sofotasios,et al.  Cache-Aided Non-Orthogonal Multiple Access for 5G-Enabled Vehicular Networks , 2019, IEEE Transactions on Vehicular Technology.

[28]  Thar Baker,et al.  Providing secure and reliable communication for next generation networks in smart cities , 2020, Sustainable Cities and Society.

[29]  Sitalakshmi Venkatraman,et al.  Use of Data Visualisation for Zero-Day Malware Detection , 2018, Secur. Commun. Networks.

[30]  Eduardo Huedo,et al.  Cross-Site Virtual Network in Cloud and Fog Computing , 2017, IEEE Cloud Computing.

[31]  Quoc-Viet Pham,et al.  A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid , 2020, IEEE Access.

[32]  Asuman Dogac,et al.  An Interoperability Test Framework for HL7-Based Systems , 2009, IEEE Transactions on Information Technology in Biomedicine.

[33]  Ali Kashif Bashir,et al.  Internet of Threats and Context Aware Security: Part Two , 2017 .

[34]  Mianxiong Dong,et al.  Foud: Integrating Fog and Cloud for 5G-Enabled V2G Networks , 2017, IEEE Network.

[35]  Mario Marchese,et al.  Statistical fingerprint‐based intrusion detection system (SF‐IDS) , 2017, Int. J. Commun. Syst..

[36]  Li Miao,et al.  A non-cooperative differential game-based security model in fog computing , 2017, China Communications.

[37]  Jonathan F. Bard,et al.  A comparison of Box-Jenkins time series models with autoregressive processes , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[38]  C. Valli,et al.  Discovering trends for the development of novel authentication applications for dementia patients , 2017 .

[39]  Craig Valli,et al.  Risk Analysis of Cloud Sourcing in Healthcare and Public Health Industry , 2018, IEEE Access.

[40]  Maurizio Dusi,et al.  Traffic classification through simple statistical fingerprinting , 2007, CCRV.

[41]  D.F. Franklin,et al.  Proposed standard IEEE P1073 Medical Information Bus: medical device to host computer interface network overview and architecture , 1989, Eighth Annual International Phoenix Conference on Computers and Communications. 1989 Conference Proceedings.

[42]  Mamoun Alazab,et al.  Big Data for Cybersecurity: Vulnerability Disclosure Trends and Dependencies , 2019, IEEE Transactions on Big Data.

[43]  Morris Sloman,et al.  A survey of trust in internet applications , 2000, IEEE Communications Surveys & Tutorials.

[44]  Kashif Saleem,et al.  Intrusion Detection System against Sink Hole Attack in Wireless Sensor Networks with Mobile Sink , 2015, 2015 12th International Conference on Information Technology - New Generations.

[45]  Malrey Lee,et al.  Dynamic Health Level 7 Packetizer for On-the-Fly Integrated Healthcare Enterprises (IHE) in Disaster Zones , 2012, ICONIP.

[46]  Arwa Alrawais,et al.  Fog Computing for the Internet of Things: Security and Privacy Issues , 2017, IEEE Internet Computing.

[47]  Aggelos Kiayias,et al.  Self-tallying Elections and Perfect Ballot Secrecy , 2002, Public Key Cryptography.

[48]  Jamal N. Al-Karaki,et al.  Security and Privacy Challenges of Integrated Disruptive Technologies , 2019, 2019 2nd International Conference on Signal Processing and Information Security (ICSPIS).

[49]  Ulrich Berger,et al.  A realizability interpretation of Church's simple theory of types , 2017, Math. Struct. Comput. Sci..

[50]  Joel J. P. C. Rodrigues,et al.  Performance evaluation of a Fog-assisted IoT solution for e-Health applications , 2019, Future Gener. Comput. Syst..

[51]  Jens Groth,et al.  Efficient Maximal Privacy in Boardroom Voting and Anonymous Broadcast , 2004, Financial Cryptography.

[52]  Usman Tariq,et al.  Sinkhole Vulnerabilities in Wireless Sensor Networks , 2014 .

[53]  Yun He,et al.  Distributed Cooperative Reinforcement Learning-Based Traffic Signal Control That Integrates V2X Networks’ Dynamic Clustering , 2017, IEEE Transactions on Vehicular Technology.

[54]  Romano Fantacci,et al.  A Cloud to the Ground: The New Frontier of Intelligent and Autonomous Networks of Things , 2016, IEEE Communications Magazine.

[55]  Mugen Peng,et al.  Fog-computing-based radio access networks: issues and challenges , 2015, IEEE Network.

[56]  H. T. Mouftah,et al.  Adaptively Supervised and Intrusion-Aware Data Aggregation for Wireless Sensor Clusters in Critical Infrastructures , 2018, 2018 IEEE International Conference on Communications (ICC).