Vulnerability Assessment, Risk, and Challenges Associated with Automated Vehicles Based on Artificial Intelligence

Artificial intelligence is the future of technology and due to the presence of artificial intelligence in the vehicle industry the growth rate has increased exponentially. With this enormous growth in the technology, various vulnerabilities also emerged that are generally neglected by the manufacturers during the manufacturing stage. The vulnerabilities are not only from the manufacturer’s side but also it can be from the developer’s side who is developing the hardware for Internet connectivity and also the software which is being used to operate the hardware which will ultimately control the vehicle. These vulnerabilities can prove to be a hacker’s paradise that is looking to infiltrate into a system like this and it will make things complicated as human lives will be at risk. This paper deals with a discussion on various security features and challenges that are emerging with the development of this technology. The paper also discusses the various risk associated with the vulnerabilities of automated vehicles and it also discuss the possible security and safety measures that can be opted to produce a safe automated vehicle. This paper also discusses the security models that are implemented by various manufacturers of the automated vehicles and it also measures the precision of those security models. The paper also deals with various intrusion detection and prevention methodologies that can be followed to control the attack on the target machine.

[1]  George Loukas,et al.  Performance Evaluation of Cyber-Physical Intrusion Detection on a Robotic Vehicle , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[2]  Nirwan Ansari,et al.  Intrusion Detection and Ejection Framework Against Lethal Attacks in UAV-Aided Networks: A Bayesian Game-Theoretic Methodology , 2017, IEEE Transactions on Intelligent Transportation Systems.

[3]  Paul Rad,et al.  Driverless vehicle security: Challenges and future research opportunities , 2020, Future Gener. Comput. Syst..

[4]  Sushanta Karmakar,et al.  A game theory based multi layered intrusion detection framework for VANET , 2018, Future Gener. Comput. Syst..

[5]  Xin Li,et al.  Algorithm and hardware implementation for visual perception system in autonomous vehicle: A survey , 2017, Integr..

[6]  Petros A. Ioannou,et al.  Intelligent Transportation Systems ConferenceIntelligent Vehicles Symposium , 2002 .

[7]  Ing-Ray Chen,et al.  Specification based intrusion detection for unmanned aircraft systems , 2012, Airborne '12.

[8]  George Loukas,et al.  Decision tree-based detection of denial of service and command injection attacks on robotic vehicles , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).

[9]  George Loukas,et al.  Behaviour-Based Anomaly Detection of Cyber-Physical Attacks on a Robotic Vehicle , 2016, 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS).

[10]  Khattab M. Ali Alheeti,et al.  An intrusion detection system against malicious attacks on the communication network of driverless cars , 2015, 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC).

[11]  Johann Schumann,et al.  R2U2: monitoring and diagnosis of security threats for unmanned aerial systems , 2017, Formal Methods Syst. Des..

[12]  Benjamin C. M. Fung,et al.  Security and privacy challenges in smart cities , 2018 .

[13]  Khattab M. Ali Alheeti,et al.  Using discriminant analysis to detect intrusions in external communication for self-driving vehicles , 2017, Digit. Commun. Networks.

[14]  Antonella Santone,et al.  Car hacking identification through fuzzy logic algorithms , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[15]  Antoine Boulanger,et al.  A simple intrusion detection method for controller area network , 2016, 2016 IEEE International Conference on Communications (ICC).

[16]  Rohit Talwar,et al.  Artificial intelligence - the next frontier in IT security? , 2017, Netw. Secur..

[17]  Khattab M. Ali Alheeti,et al.  Hybrid intrusion detection in connected self-driving vehicles , 2016, 2016 22nd International Conference on Automation and Computing (ICAC).

[18]  George Loukas,et al.  Physical indicators of cyber attacks against a rescue robot , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[19]  Vladimir Vlasov,et al.  Analysis of the main risks in the development and implementation of unmanned vehicles of urban passenger transport , 2018 .

[20]  Mirco Marchetti,et al.  Anomaly detection of CAN bus messages through analysis of ID sequences , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[21]  Ing-Ray Chen,et al.  Adaptive Intrusion Detection of Malicious Unmanned Air Vehicles Using Behavior Rule Specifications , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  George Loukas Cyber-Physical Attacks on Implants and Vehicles , 2015 .

[23]  George Loukas,et al.  Detecting Cyber-Physical Threats in an Autonomous Robotic Vehicle Using Bayesian Networks , 2017, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[24]  Sidi-Mohammed Senouci,et al.  A new Intrusion Detection Framework for Vehicular Networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[25]  W. Thomas Miller,et al.  A low-cost masquerade and replay attack detection method for CAN in automobiles , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

[26]  Seung-Hyun Kong,et al.  Cooperative Positioning Technique With Decentralized Malicious Vehicle Detection , 2018, IEEE Transactions on Intelligent Transportation Systems.

[27]  Victor A. Skormin,et al.  Unmanned Aerial Vehicle security using Recursive parameter estimation , 2014 .

[28]  Nirwan Ansari,et al.  A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[29]  D.K. Nilsson,et al.  An approach to specification-based attack detection for in-vehicle networks , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[30]  Klaus McDonald-Maier,et al.  An intelligent intrusion detection scheme for self-driving vehicles based on magnetometer sensors , 2016, 2016 International Conference for Students on Applied Engineering (ICSAE).

[31]  Hamed Fazlollahtabar,et al.  Autonomous Guided Vehicles , 2015 .

[32]  Ellen Enkel,et al.  Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices , 2016 .

[33]  Huy Kang Kim,et al.  Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network , 2016, 2016 International Conference on Information Networking (ICOIN).

[34]  Sidi-Mohammed Senouci,et al.  An accurate and efficient collaborative intrusion detection framework to secure vehicular networks , 2015, Comput. Electr. Eng..

[35]  Anupam Joshi,et al.  OBD_SecureAlert: An Anomaly Detection System for Vehicles , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).

[36]  Jeng-Farn Lee,et al.  TEAM: Trust-Extended Authentication Mechanism for Vehicular Ad Hoc Networks , 2011, IEEE Systems Journal.

[37]  Naim Asaj,et al.  Entropy-based anomaly detection for in-vehicle networks , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[38]  Shwetak N. Patel,et al.  Experimental Security Analysis of a Modern Automobile , 2010, 2010 IEEE Symposium on Security and Privacy.