IJEM Vol. 12, No. 6, 8 Dec. 2022
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Security Architecture, User Authentication, Vehicle theft, Drunk Driving
The high rate of vehicle theft and the loss of lives occasioned by drunk driving has caused irreparable losses to people and businesses, from a personal, commercial and reputation perspective. Existing systems deployed to mitigate against vehicle theft have all been breached by the ever-adaptive criminals. Drunk driving has been estimated to be a leading cause of deaths on highways and motorways, through preventable accidents. Technology has provided the tools that can aid in mitigating the vices aforementioned with the aim of provisioning lasting solutions. This paper proposes a new architecture for adaptive user authentication in order to mitigate drunk driving and vehicle theft. It considered user authentication in three (3) phases and proposed an authentication architecture for each identified phase, with a step by step description of the implementation method and tools for each phase. The architecture proposed in this study can aid in real time prevention of vehicular theft, unauthorized vehicular access and usage, while also utilizing the benefits of the latest technologies in machine vision and alcohol breadth analyzers to detect and prevent drunk driving, and the associated accidents it causes.
Edward O. Ofoegbu, "An Adaptive User Authentication Architecture for Drunk Driving and Vehicle Theft Mitigation", International Journal of Engineering and Manufacturing (IJEM), Vol.12, No.6, pp. 32-39, 2022. DOI:10.5815/ijem.2022.06.04
[1]F. Alonso, J. C. Pastor, L. Montoro and C. Esteban, "Driving under the influence of alcohol: frequency, reasons, perceived risk and punishment," Substance Abuse Treatment, Prevention, and Policy, pp. 1-9, 2015.
[2]NHTSA, "Drunk Driving," United States Department of Transportation, https://www.nhtsa.gov/risky-driving/drunk-driving, retrieved 1/11/2022, n.d..
[3]H. Summala and T. Mikkola, "Fatal accidents among car and truck drivers: Effect of fatigue, age, and alcohol consumption.," Ergonomics, p. 36:315–26., 1994.
[4]K. Smith, "Drinking and Driving," PSYCOM, https://www.psycom.net/drinking-and-driving; Retrieved 1/11/2022, n.d..
[5]J. Zhang, Z. Wang and Q. yan, "Intelligent user identity authentication in vehicle security system based on wireless signals," Complex and Intelligent Systems, 2021.
[6]B. Isong, O. Khutsoane and N. Dladlu, "Real-time Monitoring and Detection of Drinkdriving and Vehicle Over-speeding," I.J. Image, Graphics and Signal Processing, 11, pp. 1-9, 2017.
[7]R.-C. Jou and Y.-H. Lu, "Factors Affecting Recidivism of Drunk Driving for Car and Motorbike Users," Hindawi, Mathematical Problems in Engineering, pp. 1-16, 2021.
[8]H. Xu, M. Zeng, W. Hu and J. Wang, "Authentication-Based Vehicle-to-Vehicle Secure Communication for VANETs," Hindawi, Mobile Information Systems, 2019.
[9]Nissan Motor Corp, "Drunk-driving Prevention Concept Car," Nissan Motor Corp, https://www.nissan-global.com/EN/TECHNOLOGY/OVERVIEW/dpcc.html, Retrieved 1/13/2022, 2022.
[10]Insurance Information Institute, "Facts + Statistics: Auto theft," https://www.iii.org/fact-statistic/facts-statistics-auto-theft Retrieved 3/31/3022, n.d..
[11]S. F. Kolawole and A. Zakari, "Design of Anti-Vehicle Theft System using GSM and GPS with Image Acquisition," Asian Journal of Engineering and Technology (ISSN: 2321 – 2462), pp. 82-92, 2017.
[12]S. O. Aliyu, U. Abdullahi, M. Pomam, S. O. Akanmu, M. Hafiz and A. Sanusi, "Smart Protection of Vehicle using Multifactor Authentication (MFA) Technique," in 3rd International Engineering Conference (IEC 2019) , Minna, Nigeria, 2019.
[13]B. Nagendra, B. Bhargavi, K. Ramyashree, K. Sukanya and K. Nagashree, "Anti-Theft Protection of Vehicles by using Fingerprint," International Journal of Engineering Research & Technology (IJERT), 2018.
[14]K. Rohitaksha, C. G. Madhu, B. G. Nalini and C. V. Nirupama, "Android Application for Vehicle Theft Prevention and Tracking System," International Journal of Computer Science and Information Technologies, pp. 3754-3758, 2014.
[15]C. Hodge, K. Hauck and S. Gupta, "Vehicle Cybersecurity Threats and Mitigation Approaches," National Renewable Energy Laboratory, 2019.
[16]T. Jin, "Evaluation of the Effectiveness of NFC-based Anti-Theft Security System for Motorbike," International Journal of Security and Its Applications, pp. 13-20, 2016.
[17]R. Chauhan, K. K. Ghanshala and R. C. Joshi, "Convolutional Neural Network (CNN) for Image Detection and Recognition," in First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018.
[18]R. Ullah, H. Hayat, A. A. Siddiqui, U. A. Siddiqui, J. Khan, F. Ullah, S. Hassan, L. Hasan, W. Albattah, M. Islam and G. M. Karami, "A Real-Time Framework for Human Face Detection and Recognition in CCTV Images," Mathemetical Problems in Engineering , p. https://doi.org/10.1155/2022/3276704, 2022.