Published at : 27 Dec 2021
Volume : IJtech
Vol 12, No 7 (2021)
DOI : https://doi.org/10.14716/ijtech.v12i7.5399
Ivan Babkin | Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaia Street, 29, Saint Petersburg, 195251, Russia |
Olga Pisareva | The State University of Management, Ryazanskiy Prospect, 99, Moscow, 109542, Russia |
Andrey Starikovskiy | The State University of Management, Ryazanskiy Prospect, 99, Moscow, 109542, Russia |
Makhmudova Guljakhon | National University of Uzbekistan named after Mirzo Ulugbek, 4 Universitetskaya Street, Tashkent, 100174, Uzbekistan |
Yulia Anoshina | Moscow State University of Technology and Management 117418, Russia, Moscow, Novocheremushkinskaya Street, 69 |
The
economic and social infrastructure of countries is improving under the growing
influence of digital transformation, within which unmanned transportation
technologies are being developed. The increased risks involve the driverless
vehicle control and interaction mechanism, which determines the significance of
information security analysis and support in the automated traffic environment.
The technical aspect is considered in terms of a conceptual approach to
identifying a comprehensive threat model for evaluating the security of
information communications on the “driverless vehicle–road infrastructure”
technological platform. The organizational aspect embraces the requirements and
objectives related to the designing of test sites that should address a range
of problems that are concerned with the information security of automated
vehicles. The legal aspect is seen from the perspective of building a national
certification system for driverless cars and active elements of road infrastructure. This promising research
area includes work in the field of technical solutions, technological
standards, and organizational guidelines to ensure the information security of
automated transport.
Automated transport; Intelligent transport system; Information security; Threat model; Unmanned technology validation
One significant effect of
the digital transformation of the economy is the spread of connected and
automated vehicles (CAVs), including automobiles. The transfer to 5G mobile communication
and modernized solutions for 4G with the use of «Long Term Evolution» (LTE)
communication protocols has created a single technological platform of
information communication between CAVs and various elements of the external
environment in an integral digital transport system called
Vehicle-to-Everything (V2X), which is based on high-speed data exchange and
artificial intelligence methods.
This is an important
stage in the full deployment of intelligent transport systems (ITS) with
automated vehicles and related road infrastructure (RI) in urbanized spaces. To
form
The growing number of threats to the normal operation of CAVs means that
it is essential to change the standards and requirements that regulate the
creation and operation of driverless cars. The functional capabilities of
automated vehicles can be implemented, primarily if the problems of information
security (IS) are studied in the context of the digital environment of
innovative technical solutions for automobiles, transport infrastructure, and
information communications systems for traffic organization and management.
Consequently, the core of the testing problem changes because IS is assessed
when CAVs and ITS elements are validated and verified in the commissioning
process. As of today, the evolution, state, and prospects of highly automated
vehicles have been discussed by Maurer et al.
(2016). In addition, the potential of digital technology has been
analyzed by Leviäkangas (2013), Ilin et al. (2018), Bataev and Aleksandrova
(2020), Ivankova et al. (2020), and Tashenova et al. (2020), while
the technical aspects of testing the IS of CAVs have been covered by Berger (2010), Barus
et al. (2016), and Childress et al. (2016).
Some original technical solutions have been proposed by Russian scientists as
well, including Chikrin et al. (2019), who
introduced CAV positioning algorithms. The correlation between CAV technology’s effectiveness and value and the analysis of
CAVs’ effects have been presented by Hassn et al. (2016) and Economic
and Social Value of Autonomous Vehicles (2018). The safety
problems associated with unmanned
technologies have also been in the spotlight. The general approaches to this
theme are specified in Safety First for Automated Driving: A White Paper (2019). The
problems and methods of providing IS for CAVs in the ITS environment were
presented by Cui and Sabaliauskaite (2017).
The matters of risk assessment for digital technologies in cyberphysical
systems have been discussed by Grishunin et al.
(2020), and the concept of a CAV test site was validated and solutions
to its planning were presented by Szalay et al.
(2017).
The top priority of the implementation stage of unmanned transport is to
ensure the security of the CAV technology platform. This unlocks the potential
of CAVs, reduces total costs, and results in additional effects due not only to
the optimization of traffic routes, the control of fuel consumption, and the
lessened impact on the environment but also to the decrease in accidents and
reduction of financial losses. The study shows that, given the CAV design
and ITS architecture, the overall safety level of unmanned transport cannot be
improved unless IS objectives are achieved. Thus, according to the purpose of
the study, the work proposes a comprehensive approach to ensuring the IS of
CAVs based on the end-to-end use of the threat model in the process of
development, testing, and certification and to taking into account various
aspects related to the construction of the ITS. In the course of solving the
tasks that had been set, the following scientific results were obtained: (1) the
areas, factors, and conditions that contribute to the successful introduction
of autonomous vehicles were identified;
(2) the technological, organizational, and legal aspects were identified
and specified for the comprehensive approach to solving the problem of IS of
CAVs when a national ITS is created in the digital economy; (3) based on the
architecture of information interaction in the ITS presented by the authors, a
comprehensive threat model was formed to predetermine the elaboration of the
risk profile for the IS of CAVs; (4) the specification and development
of a CAV testing site was carried out to supplement the security testing tasks
and check how reliable the protection of the V2Xtechnological platform is; and (5) the composition of objectives
and schemes was determined for building a national certification system for the
IS of CAVs and RI elements in the ITS environment.
Further
research in this area should be dedicated to the system of national standards
for the ITS and methods for testing CAVs. This involves the assimilation of
national and international requirements not only to ensure CAV safety, but also
to comply with the principles of interoperability and multimodality of CAV
devices and technologies for the “seamless” building of global transport
corridors to be used for various types of transportation.
The research is partially funded by the Ministry of
Science and Higher Education of the Russian Federation under the strategic
academic leadership program Priority 2030 (Agreement 075-15-2021-1333 dated
30.09.2021).
Barus, L.S., Flores, H.M.,
Hadiwardoyo, S.P., Batoz, J.L., 2016. Intercity Mode Choice Modelling:
Considering the Intracity Transport Systems with Application to the
Jakarta-Bandung Corridor. International Journal of Technology,
Volume 7(4), pp. 581–591
Bataev, A.V., Aleksandrova, A.I.,
2020. Digitalization of the World Economy: Performance Evaluation of
Introducing Cyber-Physical Systems. In: The 9th International Conference
on Industrial Technology and Management, ICITM 2020, pp. 265–269
Berger, C., 2010. Automating
Acceptance Tests for Sensor- and Actuator-based Systems on the Example of
Autonomous Vehicles. Shaker Verlag, Aachener Informatik-Berichte, Software
Engineering, Aachen, Germany
Checkoway, S., McCoy, D., Kantor, B.,
Anderson, D., Shacham, H., Savage S., Koscher, K., Czeskis, A.,
Roesner, F., Kohno, T., 2011. Comprehensive Experimental Analyses of
Automotive Attack Surfaces. In: The
Proceedings of the 20th USENIX Conference on Security (SEC’11).
USENIX Association, Berkeley, CA, USA
Chen R., Arief M., Zhang W.,
Zhao D., 2019. How to Evaluate Proving Grounds for Self-Driving? A
Quantitative Approach. arXiv preprint, arXiv: 1903.08352. Available Online at
https://arxiv.org/pdf/1909.09079.pdf, Accessed on July 14, 2021
Chikrin, D.E., Savenkov, P.A.,
Shagiev, R.I., 2019. The Integrated Systems of High-Tech
Satellite-Local-Inertial Navigation in the Problems of Unmanned Vehicle
Control. Nanoindustry, Volume 89(5), pp. 49–56
Childress, S., Nichols, B., Charlton, B.,
Coe, S., 2016. Using an Activity-Based Model to Explore Possible Impacts
of Automated Vehicles. Journal of the Transportation Research Board, Volume 2493(1),
pp. 99–106
Cui J.,
Sabaliauskaite G., 2017. On the Alignment of Safety and Security for
Autonomous Vehicles. In:
Proceedings of IARIA CYBER, Barcelona, Spain, November, pp. 1–6
Economic and Social Value of Autonomous Vehicles, 2018. Compass
Transportation and Technology, Inc., Stackhouse, USA. 58 p. Available
Online at URL: https://avworkforce.secureenergy.org/wp-content/uploads/2018/06/Compass-Transportation-Report-June-2018.pdf
Grishunin, S.,
Suloeva, S., Burova, E., 2020. Developing
a Mechanism for Assessing Cyber Risks in Digital Technology Projects
Implemented in an Industrial Enterprise. Communications in Computer and
Information Science, Volume 1273, pp. 3–18
Hassn, H.A.H., Ismail, A.,
Borhan, M.N., Syamsunur, D., 2016. The Impact of Intelligent Transport System
Quality: Drivers’ Acceptance Perspective. International Journal of
Technology, Volume 7(4), pp. 553–561
Huang, W., Wang, K., Lv, Y.,
Zhu, F., 2016. Autonomous Vehicles Testing Methods Review. In: IEEE
19th International Conference on Intelligent Transportation Systems,
pp. 163–198
Ilin, I.V., Iliashenko, O.Y.,
Klimin, A.I., Makov, K.M., 2018. Big Data Processing in Russian Transport
Industry. In: Proceedings
of the 31st International Business Information Management
Association Conference, pp. 1967–1971
Ivankova, G.V., Mochalina, E.P.,
Goncharova, N.L., 2020. Internet of Things (IoT) in logistics. IOP
Conference Series: Materials Science and Engineering, Volume 940(1), pp. 1–7
Ivanov, M.A., Roslyj, E.B.,
Starikovskiy, A.V., Krasnikova, S.A., Shevchenko, N.A., Shustova, L.I.,
2018. Non-Binary Pseudorandom Number Generators for Information Security
Purposes. In: 8th Annual International Conference on
Biologically Inspired Cognitive Architectures, BICA 2017, Procedia Computer
Science, Volume 123, pp. 203–211
Joerger, M., Jones, C.,
Shuman, V., 2019. Testing Connected and Automated Vehicles
(CAVs): Accelerating Innovation, Integration, Deployment and Sharing Results. In:
Road Vehicles Automation, Volume 5, Meyer, G., Beiker, S.,
Shpringer (eds.), pp. 197–206
Leviäkangas, P., 2013.
Intelligent Transport Systems-Technological, Economic, System Performance and
Market Views. International Journal of Technology, Volume 4(3),
pp. 288–298
Li, L., Huang, W., Liu, Y.,
Zheng, N., Wang, F., 2016. Intelligence Testing for Autonomous
Vehicles: A New Approach. IEEE Transactions on Intelligent Vehicles, Volume 1(2), pp. 158–166
Miller, C., Valasek, C., 2014. A
Survey of Remote Automotive Attack Surfaces, Black Hat, USA
Maurer, M., Gerdes, J.,
Lenz, B., Winner, H., (eds.), 2016. Autonomous
Driving: Technical, Legal and Social Aspects, Springer,
Berlin, Germany
Okuyama, K., 2019. Formulation of a
Comprehensive Threat Model for Automated Driving Systems Including External
Vehicular Attacks such as V2X and the Establishment of an Attack Evaluation
Method through Telecommunication. In: SIP-adus: Project Reports,
2014–2018— Automated Driving for Universal Services. Publisher’s Office Cabinet
Office, Japan, pp. 77–83
Pisareva, O.M., Alexeev, V.A.,
Mednikov, D.N., Starikovsky, A.V., Kurguzov, V.B., 2021.
Creating a National Certification System for Unmanned Vehicles: Tasks of
Information Security Testing. St. Petersburg State Polytechnical University
Journal. Economics, Volume 14(2), pp. 63–80
Safety First for Automated Driving: A White
Paper, 2019. Aptiv Services US, LLC; AUDI AG;
Bayrische Motoren Werke AG; Beijing Baidu Netcom Science Technology Co., Ltd;
Continental Teves AG & Co oHG; Daimler AG; FCA US LLC; HERE Global B.V.;
Infineon Technologies AG; Intel; Volkswagen AG
Schmittner, C.,
Ma, Z., Gruber, T., 2014. Standardization
Challenges for Safety and Security of Connected, Automated and Intelligent
Vehicles. In: The 3rd International Conference on
Connected Vehicles & Expo (ICCVE 2014), pp. 941–942
Szalay, Z., Nyerges, A., Hamar, Z.,
Hesz, M., 2017. Technical Specification Methodology for an Automotive
Proving Ground Dedicated to Connected and Automated Vehicles. Periodica
Polytechnica Transportation Engineering, Volume 45(3),
pp. 168–174
Szalay, Z., Tettamanti, T., Esztergar-Kiss, D., Varga, I.,
Bartolini, C., 2018. Development of a Test Track for Driverless Cars:
Vehicle Design, Track Configuration, and Liability Considerations. Periodica
Polytechnica Transportation Engineering, Volume 46(1), pp. 29–35
Tashenova, L., Babkin, A., Mamrayeva, D.,
Babkin, I., 2020. Method for Evaluating the Digital Potential of a
Backbone Innovative Active Industrial Cluster. International Journal of
Technology, Volume 11(8), pp. 1499–1508
Threlfall R.,
2019. Autonomous Vehicles Readiness Index, KPMG International. Amstelveen,
Netherlands Available Online at https://assets.kpmg/content/dam/kpmg/xx/pdf/2019/02/2019-autonomous-vehicles-readiness-index.pdf