Cybersecurity in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are emerging field characterized by complex data model, dynamics and strict time requirements. Ensuring cybersecurity in ITS is a complex task on which the safety and efficiency of transportation depends. The imposition of standards for a comprehensive architecture, as well as specific security standards, is one of the key steps in the evolution of ITS. The article examines the general outlines of the ITS architecture and security issues. The main focus of security approaches is: configuration and initialization of the devices during manufacturing at perception layer; anonymous authentication of nodes in VANET at network layer; defense of fog-based structures at support layer and description and standardization of the complex model of data and metadata and defense of systems, based on AI at application layer. The article oversees some conventional methods as network segmentation and cryptography that should be adapted in order to be applied in ITS cybersecurity. The focus is on innovative approaches that have recently been trying to find their place in ITS security strategies. These approaches includes blockchain, bloom filter, fog computing, artificial intelligence, game theory and ontologies. In conclusion, a correlation is made between the commented methods, the problems they solve and the architectural layers in which they are applied.

[1]  Maxim O. Kalinin,et al.  Supporting connectivity of VANET/MANET network nodes and elastic software-configurable security services using blockchain with floating genesis block , 2018 .

[2]  Martin Mullins,et al.  Connected and autonomous vehicles: A cyber-risk classification framework , 2019, Transportation Research Part A: Policy and Practice.

[3]  Abdullah Al-Barakati,et al.  DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System , 2020, Applied Sciences.

[4]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[5]  Imad Mahgoub,et al.  Implementation of the WAVE 1609.2 Security Services Standard and Encountered Issues and Challenges , 2018, 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[6]  Fabio Massacci,et al.  IoT Security Configurability with Security-by-Contract , 2019, Sensors.

[7]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[8]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[9]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[10]  Byung Chul Tak,et al.  BlackEye: automatic IP blacklisting using machine learning from security logs , 2019 .

[11]  Martti Lehto,et al.  Artificial intelligence in the cyber security environment , 2019 .

[12]  Peter Kulchyski and , 2015 .

[13]  Aveek Dutta,et al.  Reputation based Routing in MANET using Blockchain , 2020, 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS).

[14]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[15]  Simon Parkinson,et al.  Fog computing security: a review of current applications and security solutions , 2017, Journal of Cloud Computing.

[16]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[17]  Ahmad Khademzadeh,et al.  Selfish node detection based on hierarchical game theory in IoT , 2019, EURASIP J. Wirel. Commun. Netw..

[18]  Ahmed Habbani,et al.  Blockchain Security in MANETs , 2019 .

[19]  Tong Li,et al.  Lightweight Cryptographic Techniques for Automotive Cybersecurity , 2018, Wirel. Commun. Mob. Comput..

[20]  J. M. Lozano Domínguez,et al.  Cybersecurity certification and auditing of automotive industry , 2020 .

[21]  Xinkai Wu,et al.  Modeling and analyzing cyberattack effects on connected automated vehicular platoons , 2020 .

[22]  Panagiotis Papadimitratos,et al.  Proactive certificate validation for VANETs , 2016, 2016 IEEE Vehicular Networking Conference (VNC).

[23]  Bidyut Gupta,et al.  Adversarial Machine Learning: Difficulties in Applying Machine Learning to Existing Cybersecurity Systems , 2020, CATA.

[24]  Mu Han,et al.  Anonymous-authentication scheme based on fog computing for VANET , 2020, PloS one.

[25]  Vasiliy S. Elagin,et al.  Blockchain Behavioral Traffic Model as a Tool to Influence Service IT Security , 2020, Future Internet.

[26]  Zeeshan Hameed Mir,et al.  LTE and IEEE 802.11p for vehicular networking: a performance evaluation , 2014, EURASIP J. Wirel. Commun. Netw..

[27]  Pierluigi Coppola,et al.  Autonomous vehicles and future mobility solutions , 2019, Autonomous Vehicles and Future Mobility.