IoT-Enabled Transportation Networks for Resilient Intrusion Detection Using Deep Learning
Keywords:
Internet of Things, Deep Learning, Convolution neural network, Intrusion detection, transportation networkAbstract
As Internet of Things (IoT) devices proliferate in transportation networks, the security and resilience of those networks are increasingly important. This study suggests a novel approach based on deep learning to effectively detect intrusions in IoT-equipped transport networks.The suggested method use convolutional neural networks (CNN), a type of deep learning technique, to automatically extract useful characteristics from the massive amounts of information generated by Internet of Things (IoT) devices in transportation networks. The system can precisely identify and classify intrusions in real time by training the CNN model on a large collection of legitimate and malicious traffic patterns.Extensive experiments made use of a realistic data base to demonstrate the efficacy of the proposed strategy for a network of things-driven transport. Despite having detected several types of intrusions, the system has maintained a good false positive rate.The proposed system for detecting persistent intrusions offers strong protection for the transportation networks driven by the Internet against new threats and ensures the continuous operation of the vital transportation infrastructure. The system can adapt to new attack vectors and increase network security overall thanks to deep learning and group learning approaches.
Downloads
References
H. Qiao, G. Li, Y. Chen., Research on key technologies of vehicle networking system architecture, Electron. Prod. 352 (11) (2018) 43–50.
H. Sedjelmaci, S.M. Senouci, M.A. Abu-Rgheff., An efficient and lightweight intrusion detection mechanism for service-oriented vehicular networks, IEEE Internet Things J. 6 (1) (2014) 570–577.
V. Golovko, P. Kochurko, Emotion recognition using neural networks, Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2009
X. Li, B. Yang., Analysis of safety protection in vehicle networking, Mob. Commun. 39 (11) (2015) 30–33.
C.F. Tsai, Y.F. Hsu, C.Y. Lin, et al., Intrusion detection by machine learning: a review, Expert Syst. Appl. 36 (10) (2009) 11994–12000.
N.M. Nawi, Nazri, M.R. Ransing, R.S. Rajesh, An improved learning algorithm based on the conjugate gradient method for back propagation neural networks, Trans. Eng. Comput. Tech. 4 (2009) 211–215.
N.M. Nawi, R.S. Ransing, M.N.M. Salleh, et al., An improved back propagation neural network algorithm on classification problems, Database Theory and Application, Bio-Science and Bio-Technology - International Conferences, 2010
S. Woo, H.J. Jo, D.H. Lee, A practical wireless attack on the connected car and security protocol for in-vehicle CAN, IEEE Trans. Intell. Transp. Syst. 16 (2) (2014) 1–14.
H.I. Ahmed, N.A. Elfeshawy, S.F. Elzoghdy, et al., A neural network-based learning algorithm for intrusion detection systems, Wireless Person. Commun. 97 (2) (2017) 3097–3112.
P. Tyagi, D. Dembla., Investigating the security threats in vehicular ad hoc networks (VANETs): towards security engineering for safer on-road transportation, IEEE International Conference on Advances in Computing, Communications and Informatics, Indian, 2014.
Cho, K.T.; Shin, K.G. Fingerprinting electronic control units for vehicle intrusion detection. In Proceedings of the 25th USENIX Security Symposium (USENIX Security 16), Austin, TX, USA, 10–12 August 2016; pp. 911–927.
Cho, K.; Shin, K.G. Viden: Attacker Identification on In-Vehicle Networks. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA, 30 October–3 November 2017.
Sagong, S.U.; Ying, X.; Poovendran, R.; Bushnell, L. Exploring attack surfaces of voltage-based intrusion detection systems in controller area networks. ESCAR Eur. 2018, 2018, 1–13.
Li, D.; Tian, M.; Jiang, R.; Yang, K. Exploiting Temperature-Varied Voltage Fingerprints for In-vehicle CAN Intrusion Detection. In Proceedings of the ACM Turing Award Celebration Conference-China (ACM TURC 2021), Hefei, China, 30 July–1 August 2021; pp. 116–120.
Taylor, A.; Japkowicz, N.; Leblanc, S. Frequency-based anomaly detection for the automotive CAN bus. In Proceedings of the 2015 World Congress on Industrial Control Systems Security (WCICSS), London, UK, 14–16 December 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 45–49. Song, H.M.; Kim, H.R.; Kim, H.K. Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network. In Proceedings of the 2016 International Conference on Information Networking (ICOIN), Kota Kinabalu, Malaysia, 13–15 January 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 63–68.
Müter, M.; Asaj, N. Entropy-based anomaly detection for in-vehicle networks. In Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, 5–9 June 2011; pp. 1110–1115.
Marchetti, M.; Stabili, D.; Guido, A.; Colajanni, M. Evaluation of anomaly detection for in-vehicle networks through information-theoretic algorithms. In Proceedings of the 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), Bologna, Italy, 7–9 September 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 1–6.
Wu, W.; Huang, Y.; Kurachi, R.; Zeng, G.; Xie, G.; Li, R.; Li, K. Sliding window optimized information entropy analysis method for intrusion detection on in-vehicle networks. IEEE Access 2018, 6, 45233–45245.
Seo, E.; Song, H.M.; Kim, H.K. Gids: Gan based intrusion detection system for in-vehicle network. In Proceedings of the 2018 16th Annual Conference on Privacy, Security and Trust (PST), Belfast, Ireland, 28–30 August 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6.
Alshammari, A.; Zohdy, M.A.; Debnath, D.; Corser, G. Classification approach for intrusion detection in vehicle systems. Wirel. Eng. Technol. 2018, 9, 79–94.
Bozdal, M.; Samie, M.; Jennions, I.K. WINDS: A wavelet-based intrusion detection system for Controller Area Network (CAN). IEEE Access 2021, 9, 58621–58633.
Derhab, A.; Belaoued, M.; Mohiuddin, I.; Kurniawan, F.; Khan, M.K. Histogram-Based Intrusion Detection and Filtering Framework for Secure and Safe In-Vehicle Networks. IEEE Trans. Intell. Transp. Syst. 2021, 23, 2366–2379.
Faruqui, N.; Yousuf, M.A.; Whaiduzzaman, M.; Azad, A.; Barros, A.; Moni, M.A. LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. Comput. Biol. Med. 2021, 139, 104961.
Wójcicki, K.; Biega ´nska, M.; Paliwoda, B.; Górna, J. Internet of Things in Industry: Research Profiling, Application, Challenges and Opportunities—A Review. Energies 2022, 15, 1806.
Hasan, M.K.; Akhtaruzzaman, M.; Kabir, S.R.; Gadekallu, T.R.; Islam, S.; Magalingam, P.; Hassan, R.; Alazab, M.; Alazab, M.A. Evolution of industry and blockchain era: Monitoring price hike and corruption using BIoT for smart government and industry 4.0. IEEE Trans. Ind. Inform. 2022, 18, 9153–9161.
Zhao, Y.; Lian, Y. Event-driven Circuits and Systems: A Promising Low Power Technique for Intelligent Sensors in AIoT Era. IEEE Trans. Circuits Syst. II Express Briefs 2022, 69, 3122–3128.
Soldatos, J.; Gusmeroli, S.; Malo, P.; Di Orio, G. Internet of things applications in future manufacturing. In Digitising the Industry Internet of Things Connecting the Physical, Digital and Virtual Worlds; River Publishers: Delft, the Netherlands, 2022; pp. 153–183.
Sharma, R.; Arya, R. Security threats and measures in the Internet of Things for smart city infrastructure: A state of art. Trans. Emerg. Telecommun. Technol. 2022, 1, e4571
Diksha Siddhamshittiwar. (2017). An Efficient Power Optimized 32 bit BCD Adder Using Multi-Channel Technique. International Journal of New Practices in Management and Engineering, 6(02), 07 - 12. https://doi.org/10.17762/ijnpme.v6i02.57
Smit, S., Popova, E., Milić, M., Costa, A., & Martínez, L. Machine Learning-based Predictive Maintenance for Industrial Systems. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/139
Dhabliya, D., & Parvez, A. (2019). Protocol and its benefits for secure shell. International Journal of Control and Automation, 12(6 Special Issue), 19-23. Retrieved from www.scopus.com Dhabliya, D., & Sharma, R. (2019). Cloud computing based mobile devices for distributed computing. International Journal of Control and Automation, 12(6 Special Issue), 1-4. doi:10.33832/ijca.2019.12.6.01
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
