Abstract
This work aims to implement a clustering scheme to separate vehicles into a cluster that is based on various parameters, such as the total number of relay nodes, mistrust value, experience-based trust, and recommendation based trust. The Fuzzy based trust recommendation policy (FBTRP) creates a trust model that enables a recommendation and trust-based communication between the relay nodes. The FBTRP policy computes the trust value of each relay/intermediate node using two parameters namely network density and distance factor of the relay. Based on this trust value obtained from the FBTRP policy, the secure relay nodes can be selected to forward the data packets from the source to the destination. The proposed trust-based mechanism uses a Deep Belief Network (DBN) to predict the vehicle's malicious behaviour in the future using the threshold value obtained and divide the vehicles into three lists namely Green (Normal), Ash (Abnormal), and Black (Malicious). This trust results derived from the FBTRP-DBN model helps to select an optimal cluster head using the highest trustworthy node present in the VANET network by eliminating the malicious ones. The main aim of this work is to prevent malicious attacks from the trusted node and prevent the VANET network from DoS attacks. The efficiency of the proposed FBTRP-DBN trust model is evaluated by comparing it with different state-of-art techniques using various performance evaluation metrics such as False Positive Rate, Detection Rate, Processing Delay, Network Accuracy, PDR, Throughput Ratio, End to End delay, Communication Overhead, and False Alarm Detection Rate.









Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Oubabas, S., Aoudjit, R., Rodrigues, J. J., & Talbi, S. (2018). Secure and stable vehicular ad hoc network clustering algorithm based on hybrid mobility similarities and trust management schemes. Vehicular Communications, 13, 128–138.
Won, M. (2020). Intelligent traffic monitoring systems for vehicle classification: A survey. IEEE Access, 8, 73340–73358.
Annur, R., & Ponnusamy, V. (2020). Information and communication technology (ICT) for intelligent transportation systems (ITS). In Employing recent technologies for improved digital governance (pp. 164–194). IGI Global.
Khatri, P., & Rajvanshi, P. R. (2020). A relative study about mobile ad-hoc network (MANET): Applications, standard, protocols, architecture, and recent trends. In IoT and Cloud computing advancements in vehicular ad-hoc networks (pp. 156–173). IGI Global.
Sundararaj, V., & Selvi, M. (2021). Opposition grasshopper optimizer based multimedia data distribution using user evaluation strategy. Multimedia Tools and Applications, 80(19), 29875–29891.
Mutlag, A. A., AbdGhani, M. K., Arunkumar, N. A., Mohammed, M. A., & Mohd, O. (2019). Enabling technologies for fog computing in healthcare IoT systems. Future Generation Computer Systems, 90, 62–78.
Abdulkareem, K. H., Mohammed, M. A., Gunasekaran, S. S., Al-Mhiqani, M. N., Mutlag, A. A., Mostafa, S. A., Ali, N. S., & Ibrahim, D. A. (2019). A review of fog computing and machine learning: Concepts, applications, challenges, and open issues. IEEE Access, 7, 153123–153140.
Mutlag, A. A., KhanapiAbdGhani, M., Mohammed, M. A., Maashi, M. S., Mohd, O., Mostafa, S. A., Abdulkareem, K. H., Marques, G., & de la Torre DÍez, I. (2020). MAFC: Multi-agent fog computing model for healthcare critical tasks management. Sensors, 20(7), 1853–1855.
Mostafa, S. A., Gunasekaran, S. S., Mustapha, A., Mohammed, M. A., & Abduallah, W. M. (2019). Modelling an adjustable autonomous multi-agent internet of things system for elderly smart home. In International conference on applied human factors and ergonomics (pp. 301–311). Springer
Zhao, Y., Zhang, X., Xie, X., Ding, Y., & Kumar, S. (2019). A verifiable hidden policy CP‐ABE with decryption testing scheme and its application in VANET. Transactions on Emerging Telecommunications Technologies, e3785.
Bangui, H., Ge, M., Buhnova, B., & Hong Trang, L. (2021). Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network. Transactions on Emerging Telecommunications Technologies, e4280.
Aissa, M., Bouhdid, B., Ben Mnaouer, A., Belghith, A., & AlAhmadi, S. (2020). SOFCluster: Safety‐oriented, fuzzy logic‐based clustering scheme for vehicular ad hoc networks. Transactions on Emerging Telecommunications Technologies, e3951.
Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers & Security, 77, 277–288.
Vinu, S. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. International Journal of Intelligent Engineering and Systems, 9(3), 117–126.
Sundararaj, V. (2019). Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. International Journal of Biomedical Engineering and Technology, 31(4), 325.
Malhi, A. K., & Batra, S. (2017). Fuzzy-based trust prediction for effective coordination in vehicular ad hoc networks. International Journal of Communication Systems, 30(6), e3111.
Kolandaisamy, R., Noor, R. M., Z’aba, M. R., Ahmedy, I., & Kolandaisamy, I. (2019). Adapted stream region for packet marking based on DDoS attack detection in vehicular ad hoc networks. The Journal of Supercomputing, 1–23.
Awan, K. A., Din, I. U., Almogren, A., Guizani, M., & Khan, S. (2020). StabTrust—A stable and centralized trust-based clustering mechanism for IoT enabled vehicular ad-hoc networks. IEEE Access, 8, 21159–21177.
Poongodi, M., Hamdi, M., Sharma, A., Ma, M., & Singh, P. K. (2019). DDoS detection mechanism using trust-based evaluation system in VANET. IEEE Access, 7, 183532–183544.
Parham, M., & Pouyan, A. A. (2020). An effective privacy-aware sybil attack detection scheme for secure communication in vehicular ad hoc network. Wireless Personal Communications, 1–34.
Bouali, T., Senouci, S. M., & Sedjelmaci, H. (2016). A distributed detection and prevention scheme from malicious nodes in vehicular networks. International Journal of Communication Systems, 29(10), 1683–1704.
Fatemidokht, H., & Rafsanjani, M. K. (2020). QMM-VANET: An efficient clustering algorithm based on QoS and monitoring of malicious vehicles in vehicular ad hoc networks. Journal of Systems and Software, 110561.
Hasrouny, H., Samhat, A. E., Bassil, C., & Laouiti, A. (2019). Misbehavior detection and efficient revocation within VANET. Journal of Information Security and Applications, 46, 193–209.
Gao, Y., Wu, H., Song, B., Jin, Y., Luo, X., & Zeng, X. (2019). A distributed network intrusion detection system for distributed denial of service attacks in vehicular ad hoc network. IEEE Access, 7, 154560–154571.
Palaniswamy, B., Camtepe, S., Foo, E., Simpson, L., Baee, M. A. R., & Pieprzyk, J. (2020). Continuous authentication for VANET. Vehicular Communications, 100255.
Tripathi, K. N., & Sharma, S. C. (2019). A trust based model (TBM) to detect rogue nodes in vehicular ad-hoc networks (VANETS). International Journal of System Assurance Engineering and Management, 1–15.
Carl, G., Kesidis, G., Brooks, R. R., & Rai, S. (2006). Denial-of-service attack-detection techniques. IEEE Internet Computing, 10(1), 82–89.
Ghaleb, F. A., Maarof, M. A., Zainal, A., Al-Rimy, B. A. S., Saeed, F., & Al-Hadhrami, T. (2019). Hybrid and multifaceted context-aware misbehavior detection model for vehicular ad hoc network. IEEE Access, 7, 159119–159140.
Chowdhury, A., Karmakar, G., & Kamruzzaman, J. (2019). Trusted autonomous vehicle: Measuring trust using on-board unit data. In 2019 18th IEEE International conference on trust, security and privacy in computing and communications/13th IEEE international conference on big data science and engineering (TrustCom/BigDataSE) (pp. 787–792). IEEE.
Sou, S. I., & Tonguz, O. K. (2011). Enhancing VANET connectivity through roadside units on highways. IEEE Transactions on Vehicular Technology, 60(8), 3586–3602.
Uzcátegui, R. A., De Sucre, A. J., & Acosta-Marum, G. (2009). Wave: A tutorial. IEEE Communications Magazine, 47(5), 126–133.
Hamalainen, P., Alho, T., Hannikainen, M., & Hamalainen, T. D. (2006). Design and implementation of low-area and low-power AES encryption hardware core. In 9th EUROMICRO conference on digital system design (DSD'06) (pp. 577–583). IEEE.
Jiang, D., & Delgrossi, L. (2008). IEEE 802.11 p: Towards an international standard for wireless access in vehicular environments. In VTC Spring 2008-IEEE vehicular technology conference (pp. 2036–2040). IEEE.
Xu, F., Fang, Z., Tang, R., Li, X., & Tsui, K. L. (2020). An unsupervised and enhanced deep belief network for bearing performance degradation assessment. Measurement, 107902.
Mohsin, M., Li, H., & Abdalla, H. B. (2020). Optimization driven Adam-Cuckoo search-based deep belief network classifier for data classification. IEEE Access, 8, 105542–105560.
Li, L., Sheng, X., Du, B., Wang, Y., & Ran, B. (2020). A deep fusion model based on restricted Boltzmann machines for traffic accident duration prediction. Engineering Applications of Artificial Intelligence, 93, 103686.
Fischer, A., & Igel, C. (2011). Bounding the bias of contrastive divergence learning. Neural Computation, 23(3), 664–673.
Gelfand, A. E. (2000). Gibbs sampling. Journal of the American Statistical Association, 95(452), 1300–1304.
Breitung, K. (1991). Probability approximations by log likelihood maximization. Journal of Engineering Mechanics, 117(3), 457–477.
Jerbi, M., Mohamed-Rasheed, T., & Senouci, S.-M. (2012). Method for estimating and signalling the density of mobile nodes in a road network. U.S. Patent No. 8,208,382.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There are no conflicts of interest for authors to publish their article in the journal.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tripathi, K.N., Yadav, A.M. & Sharma, S.C. Fuzzy and Deep Belief Network Based Malicious Vehicle Identification and Trust Recommendation Framework in VANETs. Wireless Pers Commun 124, 2475–2504 (2022). https://doi.org/10.1007/s11277-022-09474-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-022-09474-8