Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Addressing Malware Attacks on Connected and Autonomous Vehicles: Recent Techniques and Challenges

  • Chapter
  • First Online:
Malware Analysis Using Artificial Intelligence and Deep Learning

Abstract

Part of the wider development and monitoring of smart environments for an intelligent cities approach is the building of an intelligent transportation system. Such a system involves the development of modern vehicles which significantly improve passenger safety and comfort, a trend that is expected to increase in the coming years. There are key factors relating to safety impacts and security vulnerabilities that may emerge during the increased deployment of automated vehicles and the security and privacy of connected and automated vehicle systems. They include ways of defining the security of malware-relevant system boundaries including electronic control units, silicon hardware, software, vehicle systems, infrastructure, network connectivity and more. In addition, vehicle industries are facing many problems with critical security and privacy issues, influenced by the smart environments for an intelligent cities approach. Such problems are related to hardware and software applications that allow the interfacing of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure networks (V2I). In this chapter, we present connected car methods relating to the attack, defence and detection of malware in vehicles. Critical issues are introduced regarding the sharing of safety information and the verification of the integrity of this information from V2V and V2I networks. In particular, we discuss the challenges and review state-of-the-art intra–inter-vehicle communication. Hackers can access this information in V2V/V2I networks and broadcast fake messages and malware to break the security system by using weak points in vehicles and networks. We present important security approaches that are used in vehicles which can fully protect the vehicle security architecture by detecting the attempts made and the methods used by hackers to tackle malware and security problems in vehicles. We present a comprehensive overview of current research on advanced intra-inter-vehicle communication networks and identify outstanding research questions that may be used to achieve high levels of vehicle security and privacy in intelligent cities in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Saber A., F. Di Troia, and M. Stamp. 2020. Intrusion detection and can vehicle networks. Digital Forensic Investigation of Internet of Things (IoT) Devices.

    Google Scholar 

  2. Abbas, Khamael. 2011. Eye recognition technique based on eigeneyes method. International Conference on Software and Computer Applications, vol. 9, 212–219. Singapore: IACSIT Press.

    Google Scholar 

  3. Adam. Tire pressure, 2011. Retrieved from: http://www.bergenimports.com/tire-pressure.

  4. Khamael Abbas Al-Dulaimi., and Aiman Abdul Razzak Al-Saba’awi. 2011. Handprint recognition technique based on image segmentation for recognize. International Journal of Computer Information Systems 2 (6): 7–12.

    Google Scholar 

  5. Al-Sabaawi, A., H.M. Ibrahim, M.A.B.M. Almalullah, J. Kaur, K. Al-Dulaimi, and A. Zwayen. 2019. Proposal specifications of building data centre for virtual globalnets. In 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 1–7.

    Google Scholar 

  6. Al-Sabaawi, Aiman, and Ernest Foo. 2019. A comparison study of android mobile forensics for retrieving files system. International Journal of Computer Science and Security (IJCSS) 13 (4): 148.

    Google Scholar 

  7. Al-Sabaawi, Aiman Abdul-Razzak Fatehi. 2017. Traffic congestion control based in-memory analytics: Challenges and advantages. International Journal of Computer Applications 975: 8887.

    Google Scholar 

  8. M. Alazab, S. Venkatraman, and P. Watters. 2013. Information Security Governance: The Art of Detecting Hidden Malware. IGI Global.

    Google Scholar 

  9. Ali, S.M., and Khamael A. AL-Phalahi. 2009. Face recognition technique based on eigenfaces method. In 3rd Scientific Conference of the College of Science-Baghdad University-Iraq, 781–785.

    Google Scholar 

  10. Atombo, C., C. Wu, H. Zhang, and A.A. Agbo. 2017. Drivers speed selection behaviors, intention, and perception towards the use of advanced vehicle safety. Advances in transportation studies 42: 23–38.

    Google Scholar 

  11. Azab, A., M. Alazab, and M. Aiash. 2016. Machine learning based botnet identification traffic. In 2016 IEEE Trustcom/BigDataSE/ISPA, 1788–1794.

    Google Scholar 

  12. Azab, A., R. Layton, M. Alazab, and J. Oliver. 2014. Mining malware to detect variants. In 2014 Fifth Cybercrime and Trustworthy Computing Conference, 44–53.

    Google Scholar 

  13. Bécsi, Tamás, Szilárd Aradi, and Péter Gáspár. 2015. Security issues and vulnerabilities in connected car systems. In 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 477–482. IEEE.

    Google Scholar 

  14. Bhaskar, Ashish, and Edward Chung. 2013. Fundamental understanding on the use of bluetooth scanner as a complementary transport data. Transportation Research Part C: Emerging Technologies 37: 42–72.

    Article  Google Scholar 

  15. Caceres, N., J.P. Wideberg, and F.G. Benitez. 2007. Deriving origin-destination data from a mobile phone network. IET Intelligent Transport Systems 1 (1): 15–26.

    Article  Google Scholar 

  16. Francesco Calabrese, Mi Diao, Giusy Di Lorenzo, Joseph Ferreira Jr, and Carlo Ratti. 2013. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies, 26:301–313.

    Google Scholar 

  17. Robert N Charette. 2009. This car runs on code. IEEE spectrum 46 (3):3.

    Google Scholar 

  18. Stephen Checkoway, Damon McCoy, Brian Kantor, Danny Anderson, Hovav Shacham, Stefan Savage, Karl Koscher, Alexei Czeskis, Franziska Roesner, Tadayoshi Kohno, et al. 2011. Comprehensive experimental analyses of automotive attack surfaces. In USENIX Security Symposium, vol. 4, 447–462. San Francisco.

    Google Scholar 

  19. Simranpreet Singh Chhatwal., and Manmohan Sharma. 2015. Detection of impersonation attack in vanets using buck filter and vanet content fragile watermarking (vcfw). In 2015 International Conference on Computer Communication and Informatics (ICCCI), 1–5. IEEE.

    Google Scholar 

  20. Danalet, Antonin, Bilal Farooq, and Michel Bierlaire. 2014. A bayesian approach to detect pedestrian destination-sequences from wifi signatures. Transportation Research Part C: Emerging Technologies 44: 146–170.

    Article  Google Scholar 

  21. Dedes, G., S. Wolfe, D. Guenther, Byungkyu Brian Park, J.J. So, K. Mouskos, D. Grejner-Brzezinska, C. Toth, X. Wang, and G. Heydinger. 2011. A simulation design of an integrated gnss/inu, vehicle dynamics, and microscopic traffic flow simulator for automotive safety. Advances in Transportation Studies.

    Google Scholar 

  22. Duraisamy, Roshan, Zoran Salcic, Maurizio Adriano Strangio, and Miguel Morales-Sandoval. 2007. An elliptic curve key establishment protocol-on-chip. Supporting symmetric 128-bit aes in networked embedded systems. EURASIP Journal on Embedded Systems 2007: 1–9.

    Google Scholar 

  23. Fysarakis, Konstantinos, Ioannis Askoxylakis, Vasilios Katos, Sotiris Ioannidis, and Louis Marinos. 2017. Security concerns in co-operative intelligent transportation systems.

    Google Scholar 

  24. Greenberg, Andy. 2015. Hackers remotely kill a jeep on the highway–with me in it. Wired 7: 21.

    Google Scholar 

  25. Harborfreigh. OBD II and can code reader with multilingual menu, 2016. Retrieved from: http://www.harborfreight.com/can-obdii-code-reader-with-multilingual-menu98568.html.

  26. Hubaux, Jean-Pierre, Srdjan Capkun, and Jun Luo. 2004. The security and privacy of smart vehicles. IEEE Security and Privacy 2 (3): 49–55.

    Article  Google Scholar 

  27. Islam, Mhafuzul, Mashrur Chowdhury, Hongda Li, and Hu Hongxin. 2018. Cybersecurity attacks in vehicle-to-infrastructure applications and their prevention. Transportation Research Record 2672 (19): 66–78.

    Article  Google Scholar 

  28. Henrik Johansson, Karl, Martin Törngren, and Lars Nielsen. 2005. Vehicle applications of controller area network. In Handbook of Networked and Embedded Control Systems, 741–765. Springer.

    Google Scholar 

  29. Koopman. S. 2015. Automotive advanced driver assistance systems ADAS market will reach \$ 18.2bn in 2014. According to a New Study on ASDReports, 2015. https://www.asdreports.com/news-5198/automotive-advanced-driver-assistance-systems-adas-market-will-reach-182bn-2014-according-new-study-asdreports.

  30. Koscher, Karl, Alexei Czeskis, Franziska Roesner, Shwetak Patel, Tadayoshi Kohno, Stephen Checkoway, Damon McCoy, Brian Kantor, Danny Anderson, Hovav Shacham, et al. 2010. Experimental security analysis of a modern automobile. In 2010 IEEE Symposium on Security and Privacy, 447–462. IEEE.

    Google Scholar 

  31. Reger, L. 2015. Advances in automotive at ces 2015. Retrieved fromhttps://blog.nxp.com/automotive/advances-in-automotive-at-ces-2015.

  32. Lesser, Victor, Charles L Ortiz Jr, and Milind Tambe. 2012. Distributed Sensor Networks: A Multiagent Perspective, vol. 9. Springer Science & Business Media.

    Google Scholar 

  33. Lu, Ning, Nan Cheng, Ning Zhang, Xuemin Shen, and Jon W Mark. 2014. Connected vehicles: Solutions and challenges. IEEE Internet of Things Journal 1 (4):289–299.

    Google Scholar 

  34. Maglaras, Leandros A, Ali H Al-Bayatti, Ying He, Isabel Wagner, and Helge Janicke. Social internet of vehicles for smart cities. Journal of Sensor and Actuator Networks 5 (1):3.

    Google Scholar 

  35. Maglaras, Leandros A, Pavlos Basaras, and Dimitrios Katsaros. 2013. Exploiting vehicular communications for reducing co2 emissions in urban environments. In 2013 International Conference on Connected Vehicles and Expo (ICCVE) 32–37. IEEE.

    Google Scholar 

  36. Malla, Adil Mudasir, and Ravi Kant Sahu. 2013. Security attacks with an effective solution for dos attacks in vanet. International Journal of Computer Applications 66 (22).

    Google Scholar 

  37. Maurizio. Hacking car security system and remote keyless entry, 2015. Retrieved from: http://dev.emcelettronica.com/hacking-car-security-system-and-remote-keyentry-rke.

  38. Menouar, Hamid, Ismail Guvenc, Kemal Akkaya, A. Selcuk Uluagac, Abdullah Kadri, and Adem Tuncer. 2017. Uav-enabled intelligent transportation systems for the smart city: Applications and challenges. IEEE Communications Magazine 55 (3):22–28.

    Google Scholar 

  39. Mokhtar, Bassem, and Mohamed Azab. 2015. Survey on security issues in vehicular ad hoc networks. Alexandria Engineering Journal 54 (4): 1115–1126.

    Article  Google Scholar 

  40. Nolte, Thomas, Hans Hansson, and Lucia Lo Bello. 2005. Automotive communications-past, current and future. In 2005 IEEE Conference on Emerging Technologies and Factory Automation, vol. 1, 8. IEEE.

    Google Scholar 

  41. Paganini, P. 2013. Car hacking is today possible due the massive introduction of technology in our vehicles, 2013. Retrieved from: https://www.cyberdefensemagazine.com/car-hacking-is-today-possible-due-the-massive-introduction-of-technology-in-our-vehicles.

  42. Paruchuri, Vineetha. 2011. Inter-vehicular communications: Security and reliability issues. In ICTC 2011, 737–741. IEEE.

    Google Scholar 

  43. Porter, Donovan. 2018. 100base-t1 ethernet: the evolution of automotive networking. Texas Instruments: Techn. Ber.

    Google Scholar 

  44. Qian, Yi, Kejie Lu, and Nader Moayeri. 2008. Performance evaluation of a secure mac protocol for vehicular networks. In MILCOM 2008-2008 IEEE Military Communications Conference, 1–6. IEEE.

    Google Scholar 

  45. Raya, Maxim, and Jean-Pierre Hubaux. 2005. Security aspects of inter-vehicle communications. In 5th Swiss Transport Research Conference (STRC), number CONF.

    Google Scholar 

  46. Rizvi, Syed, Jonathan Willet, Donte Perino, Seth Marasco, and Chandler Condo. 2017. A threat to vehicular cyber security and the urgency for correction. Procedia Computer Science 114: 100–105.

    Article  Google Scholar 

  47. Saed, Mustafa, Scott Bone, and John Robb. 2014. Security concepts and issues in intra-inter vehicle communication network. In Proceedings of the International Conference on Security and Management (SAM), 1. The Steering Committee of The World Congress in Computer Science, Computer.

    Google Scholar 

  48. Sagstetter, Florian, Martin Lukasiewycz, Sebastian Steinhorst, Marko Wolf, Alexandre Bouard, William R Harris, Somesh Jha, Thomas Peyrin, Axel Poschmann, and Samarjit Chakraborty. 2013. Security challenges in automotive hardware/software architecture design. In 2013 Design, Automation and Test in Europe Conference and Exhibition (DATE), 458–463. IEEE.

    Google Scholar 

  49. Samuel. G. 2015. Jeep owners urged to update their cars after hackers take remote control. https://www.theguardian.com/technology/2015/jul/21/jeep-owners-urged-update-car-software-hackers-remote-control.

  50. Sparwasser, Nils, Markus Stöbe, Hartmut Friedl, Thomas Krauß, and Robert Meisner. 2007. Simworld–automatic generation of realistic landscape models for real time simulation environments–a remote sensing and gis-data based processing chain. Advances in Transportation Studies 21.

    Google Scholar 

  51. Stallings, W. 2017. Cryptography and network security: principles and practice. Upper Saddle River: Pearson.

    Google Scholar 

  52. Sumalee, Agachai, and Hung Wai Ho. 2018. Smarter and more connected: Future intelligent transportation system. IATSS Research 42 (2): 67–71.

    Google Scholar 

  53. Ahmed Sumra, Irshad, Halabi Bin Hasbullah, Iftikhar Ahmad, Daniyal M Alghazzawi, et al. 2013. Classification of attacks in vehicular ad hoc network (vanet). International Information Institute (Tokyo). Information 16 (5):2995.

    Google Scholar 

  54. Sun, Dihua, Hong Luo, Fu Liping, Weining Liu, Xiaoyong Liao, and Min Zhao. 2007. Predicting bus arrival time on the basis of global positioning system data. Transportation Research Record 2034 (1): 62–72.

    Article  Google Scholar 

  55. Tuohy, Shane, Martin Glavin, Ciarán Hughes, Edward Jones, Mohan Trivedi, and Liam Kilmartin. 2014. Intra-vehicle networks: A review. IEEE Transactions on Intelligent Transportation Systems 16 (2): 534–545.

    Article  Google Scholar 

  56. Vanajakshi, Lelitha, Shankar C Subramanian, and R Sivanandan. 2009. Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses. IET Intelligent Transport Systems 3 (1):1–9.

    Google Scholar 

  57. Wolf, Marko, André Weimerskirch, and Thomas Wollinger. 2007. State of the art: Embedding security in vehicles. EURASIP Journal on Embedded Systems 1–16: 2007.

    Google Scholar 

  58. Woo, Samuel, Hyo Jin Jo, and Dong Hoon Lee. 2014. A practical wireless attack on the connected car and security protocol for in-vehicle can. IEEE Transactions on Intelligent Transportation Systems 16 (2):993–1006.

    Google Scholar 

  59. Yadav, Aastha, Gaurav Bose, Radhika Bhange, Karan Kapoor, NCSN Iyengar, and Ronnie D Caytiles. 2016. Security, vulnerability and protection of vehicular on-board diagnostics. International Journal of Security and Its Applications 10 (4):405–422.

    Google Scholar 

  60. Yang, Teng, Frank Wolff, and Chris Papachristou. 2018. Connected car networking. In NAECON 2018-IEEE National Aerospace and Electronics Conference, 60–64. IEEE.

    Google Scholar 

  61. Yoshida, J. 2013. How hackers can take control over your car. EE Times. https://www.eetimes.com/document.asp?doc_id=1318838&page_number=2.

  62. Zhang, Chenxi, Rongxing Lu, Xiaodong Lin, P-H Ho, and Xuemin Shen. 2008. An efficient identity-based batch verification scheme for vehicular sensor networks. In IEEE INFOCOM 2008-The 27th Conference on Computer Communications, 246–250. IEEE.

    Google Scholar 

  63. Zhang, Yingchen, Penn Markham, Tao Xia, Lang Chen, Yanzhu Ye, Wu Zhongyu, Zhiyong Yuan, Lei Wang, Jason Bank, Jon Burgett, et al. 2010. Wide-area frequency monitoring network (fnet) architecture and applications. IEEE Transactions on Smart Grid 1 (2): 159–167.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Queensland University of Technology, Queensland, Australia

    Aiman Al-Sabaawi & Khamael Al-Dulaimi

  2. Department of Computer Science, Al-Nahrain University, Baghdad, Iraq

    Aiman Al-Sabaawi & Khamael Al-Dulaimi

  3. School of Information and Communication Technology, Griffith University, Queensland, Australia

    Ernest Foo

  4. College of Engineering, Information Technology and Environment, Charles Darwin University, Northern Australia, Australia

    Mamoun Alazab

Authors
  1. Aiman Al-Sabaawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khamael Al-Dulaimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ernest Foo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mamoun Alazab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aiman Al-Sabaawi .

Editor information

Editors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, CA, USA

    Mark Stamp

  2. College of Engineering, IT & Environment, Charles Darwin University, Darwin, NT, Australia

    Mamoun Alazab

  3. Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Techology, Gjøvik, Norway

    Andrii Shalaginov

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Al-Sabaawi, A., Al-Dulaimi, K., Foo, E., Alazab, M. (2021). Addressing Malware Attacks on Connected and Autonomous Vehicles: Recent Techniques and Challenges. In: Stamp, M., Alazab, M., Shalaginov, A. (eds) Malware Analysis Using Artificial Intelligence and Deep Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-62582-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-62582-5_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62581-8

  • Online ISBN: 978-3-030-62582-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation