Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(269.81KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
A Review on Data Falsification-Based attacks In Cooperative Intelligent Transportation Systems
Sultan Ahmed Almalki, Jia Song
Pages - 22 - 37     |    Revised - 30-04-2020     |    Published - 01-06-2020
Volume - 14   Issue - 2    |    Publication Date - June 2020  Table of Contents
MORE INFORMATION
KEYWORDS
Cooperative Intelligent Transportation Systems, Internet of Things, Intrusion Detection Systems, Misbehavior Detection Systems, Smart Car.
ABSTRACT
Cooperative Intelligent Transportation System (cITS) is one of IoT applications whose purpose is to enhance drive safety and efficiency. Several components constitute cITS including vehicles, road side units and backend systems. Like many IoT applications and systems, cITSs are susceptible to a wide-range of intruding or misbehaving attacks that could be launched by attackers from inside or outside of the network. Once a vehicle is compromised, it can be used to launch several types of attacks against other vehicles and/or components of cITS. They can also be used to send false information and messages to the neighboring vehicles, causing severe complications such as traffic congestions and accidents. Such attacks impede the momentum of the integration of cITS technology with existing infrastructure. In this paper, a comprehensive and deep analysis of the state-of-the-art solutions in intrusion and misbehavior detection for cITS have been conducted. This paper mainly focuses on the data falsification-based attacks that manipulate the mobility data and messages shared with the neighboring vehicles as it is more challenging and difficult to identify and mitigate. The paper can be of great use for research community to explore more opportunities and new avenues and propose more robust and effective security solutions that protect the potential applications in cITSs.
1 M. A. Khan and K. Salah, "IoT security: Review, blockchain solutions, and open challenges," Future Generation Computer Systems, vol. 82, pp. 395-411, 2018/05/01/ 2018, doi: https://doi.org/10.1016/j.future.2017.11.022.
2 J. Granjal, E. Monteiro, and J. S. Silva, "Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues," IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1294-1312, 2015, doi: 10.1109/COMST.2015.2388550.
3 Y. Chen, S. Kar, and J. M. F. Moura, "The Internet of Things: Secure Distributed Inference," IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 64-75, 2018, doi: 10.1109/MSP.2018.2842097.
4 R. W. v. d. Heijden, S. Dietzel, T. Leinmüller, and F. Kargl, "Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 779-811, 2019, doi: 10.1109/COMST.2018.2873088.
5 F. A. Ghaleb, M. A. Maarof, A. Zainal, B. A. S. Al-Rimy, F. Saeed, and T. Al-Hadhrami, "Hybrid and Multifaceted Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network," IEEE Access, vol. 7, pp. 159119-159140, 2019, doi: 10.1109/ACCESS.2019.2950805.
6 T. ETSI, "Intelligent Transport Systems (ITS); Vehicular Communications; GeoNetworking; Part 4: Geographical addressing and forwarding for point-to-point and point-to-multipoint communications; Sub-part 2: Media-dependent functionalities for ITS-G5," ETSI TS, vol. 102, pp. 636-4, 2013.
7 J. B. Kenney, "Dedicated short-range communications (DSRC) standards in the United States," Proceedings of the IEEE, vol. 99, no. 7, pp. 1162-1182, 2011.
8 A. F. Ghaleb, A. Zainal, A. M. Rassam, and F. Saeed, "Driving-situation-aware adaptive broadcasting rate scheme for vehicular ad hoc network," Journal of Intelligent & Fuzzy Systems, no. Preprint, pp. 1-16, 2018.
9 F. A. Ghaleb, M. Aizaini Maarof, A. Zainal, M. A. Rassam, F. Saeed, and M. Alsaedi, "Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages," Vehicular Communications, vol. 20, p. 100186, 2019/12/01/ 2019, doi: https://doi.org/10.1016/j.vehcom.2019.100186.
10 F. A. Ghaleb, A. Zainal, M. A. Rassam, and F. Mohammed, "An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications," in 2017 IEEE Conference on Application, Information and Network Security (AINS), 13-14 Nov. 2017 2017, pp. 13-18, doi: 10.1109/AINS.2017.8270417.
11 T. Pandiangan, I. Bali, and A. Silalahi, "Early Lung Cancer Detection Using Artificial Neural Network," Atom Indonesia, vol. 45, no. 1, pp. 9-15, 2019.
12 M. Aloqaily, S. Otoum, I. A. Ridhawi, and Y. Jararweh, "An intrusion detection system for connected vehicles in smart cities," Ad Hoc Networks, vol. 90, p. 101842, 2019/07/01/ 2019, doi: https://doi.org/10.1016/j.adhoc.2019.02.001.
13 N. Kumar and N. Chilamkurti, "Collaborative trust aware intelligent intrusion detection in VANETs," Computers & Electrical Engineering, vol. 40, no. 6, pp. 1981-1996, 2014/08/01/ 2014, doi: https://doi.org/10.1016/j.compeleceng.2014.01.009.
14 H. Sedjelmaci and S. M. Senouci, "An accurate and efficient collaborative intrusion detection framework to secure vehicular networks," Computers & Electrical Engineering, vol. 43, pp. 33-47, 2015/04/01/ 2015, doi: https://doi.org/10.1016/j.compeleceng.2015.02.018.
15 B. Subba, S. Biswas, and S. Karmakar, "A game theory based multi layered intrusion detection framework for VANET," Future Generation Computer Systems, vol. 82, pp. 12-28, 2018/05/01/ 2018, doi: https://doi.org/10.1016/j.future.2017.12.008.
16 T. Zhang and Q. Zhu, "Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs," IEEE Transactions on Signal and Information Processing over Networks, vol. 4, no. 1, pp. 148-161, 2018, doi: 10.1109/TSIPN.2018.2801622.
17 H. Hasrouny, A. E. Samhat, C. Bassil, and A. Laouiti, "Misbehavior detection and efficient revocation within VANET," Journal of Information Security and Applications, vol. 46, pp. 193-209, 2019/06/01/ 2019, doi: https://doi.org/10.1016/j.jisa.2019.03.001.
18 J. Zacharias and S. Fröschle, "Misbehavior detection system in VANETs using local traffic density," in 2018 IEEE Vehicular Networking Conference (VNC), 5-7 Dec. 2018 2018, pp. 1-4, doi: 10.1109/VNC.2018.8628321.
19 P. K. Singh, S. Gupta, R. Vashistha, S. K. Nandi, and S. Nandi, "Machine Learning Based Approach to Detect Position Falsification Attack in VANETs," Singapore, 2019: Springer Singapore, in Security and Privacy, pp. 166-178.
20 F. A. Ghaleb, M. A. Maarof, A. Zainal, B. A. S. Alrimy, A. Alsaeedi, and W. Boulila, "Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network," Remote Sensing, vol. 11, no. 23, p. 2852, 2019. [Online]. Available: https://www.mdpi.com/2072-4292/11/23/2852.
21 X. Zhang, C. Lyu, Z. Shi, D. Li, N. N. Xiong, and C. Chi, "Reliable Multiservice Delivery in Fog-Enabled VANETs: Integrated Misbehavior Detection and Tolerance," IEEE Access, vol. 7, pp. 95762-95778, 2019, doi: 10.1109/ACCESS.2019.2928365.
22 C. Zhang, K. Chen, X. Zeng, and X. Xue, "Misbehavior Detection Based on Support Vector Machine and Dempster-Shafer Theory of Evidence in VANETs," IEEE Access, vol. 6, pp. 59860-59870, 2018, doi: 10.1109/ACCESS.2018.2875678.
23 H. Amirat, N. Lagraa, C. A. Kerrach, and Y. Ouinten, "Fuzzy Clustering for Misbehaviour Detection in VANET," in 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT), 27-31 Oct. 2018 2018, pp. 200-204, doi: 10.1109/SaCoNeT.2018.8585454.
24 K. Sharshembiev, S. Yoo, E. Elmahdi, Y. Kim, and G. Jeong, "Fail-Safe Mechanism Using Entropy Based Misbehavior Classification and Detection in Vehicular Ad Hoc Networks," in 2019 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), 14-17 July 2019 2019, pp. 123-128, doi: 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00042.
25 So, Steven, Prinkle Sharma, and Jonathan Petit. "Integrating plausibility checks and machine learning for misbehavior detection in vanet." 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018.
Mr. Sultan Ahmed Almalki
Computer Science Department, University of Idaho, Moscow - United States of America
alma6989@vandals.uidaho.edu
Dr. Jia Song
Computer Science Department, University of Idaho, Moscow - United States of America