Deep Reinforcement Learning Routing Engine and Threat Detection in Vehicular Networks through Federated Intelligence and Blockchain-Based Trust Ledger

Authors

  • Parveen Akhther. A
  • A. Maryposonia
  • Prasanth. V. S

Abstract

Vehicular Ad Hoc Networks (VANETs) defines a self-organizing network formed between vehicles in roadside infrastructure for facilitating real time decision making on road. However in real time trusted updates it can introduce a latency in high vehicular traffic conditions. To address these constrains, developed a blockchain based federated system with intrusion detection to secure the data flow of vehicular environment. Initially, Road Side Unit (RSU) and vehicle contains a Federated Learning Node (FLN), Edge Key Management Unit (EKMU), and access to the Blockchain-Backed Trust Ledger (BBTL). FLN gathers data, trains a local machine learning model and transfers encrypted data to an aggregator. The EKMU creates a lightweight cryptographic key pair and sets attaches a pseudonymized identity and a trust score on the blockchain. Truncated Polynomial Ring Unit (NTPRU) used in EKMU generates a lightweight cryptographic key pair. At the same time, the federated intrusion detection system uses the lightweight XGBoost which continuously monitors traffic patterns and the data points of behaviors while looking for abnormalities (anomalies), such as spoofing, replay attacks, or false-data injections. If found a threat, Federated Intrusion Detection System (FIDS) produces alerts and reports to BBTL. Finally, trust scores are constantly classified using DNN-19 and updated in BBTL. Then the Deep Reinforcement Learning Routing Engine (DRL-RE) can use these updated trust-based metrics to construct secure and adaptive routing decisions. The proposed strategy achieved recall of 97.91%, NPV of 97.62%, error of 2.240% and accuracy of 97.5% respectively. The proposed approach accomplishes secure Vehicular communication for employing a decentralized learning-based approach for enables a real-time threat detection for more resource resilience in intelligent transportation systems

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References

Alharthi, A., Ni, Q., & Jiang, R. (2021). A privacy-preservation framework based on biometrics blockchain (BBC) to prevent attacks in VANET. Ieee Access, 9, 87299-87309.

Bibi, A., Jabbar, S., Saeed, Y., Iqbal, M. M., Ahmad, A., Akbar, H., & Qureshi, I. (2024). TR-Block: a trustable content delivery approach in VANET through Blockchain. IEEE Access.

Ali, A., Iqbal, M. M., Jabbar, S., Asghar, M. N., Raza, U., & Al-Turjman, F. (2022). VABLOCK: A blockchain-based secure communication in V2V network using icn network support technology. Microprocessors and Microsystems, 93, 104569.

Gayathri, M., & Gomathy, C. (2022). AI-TASFIS: an approach to secure vehicle-to-vehicle communication. Applied Artificial Intelligence, 36(1), 2145636.

Chandramohan, K., Manikandan, A., Ramalingam, S., & Dhanapal, R. (2024). Performance evaluation of VANET using directional location aided routing (D-LAR) protocol with sleep scheduling algorithm. Ain Shams Engineering Journal, 15(3), 102458.

Abualola, H., Otrok, H., Mizouni, R., & Singh, S. (2022). A V2V charging allocation protocol for electric vehicles in VANET. Vehicular Communications, 33, 100427.

Ali, I., Chen, Y., Ullah, N., Kumar, R., & He, W. (2021). An efficient and provably secure ECC-based conditional privacy-preserving authentication for vehicle-to-vehicle communication in VANETs. IEEE Transactions on Vehicular Technology, 70(2), 1278-1291.

Sepasgozar, S. S., & Pierre, S. (2022). Network traffic prediction model considering road traffic parameters using artificial intelligence methods in VANET. IEEE Access, 10, 8227-8242.

Singh, G. D., Prateek, M., Kumar, S., Verma, M., Singh, D., & Lee, H. N. (2022). Hybrid genetic firefly algorithm-based routing protocol for VANETs. IEEE Access, 10, 9142-9151.

Sharma, M., Kumar, P., & Tomar, R. S. (2022). Weight-based clustering algorithm for military vehicles communication in VANET. SAIEE Africa Research Journal, 114(1), 25-34.

Kandali, K., Bennis, L., El Bannay, O., & Bennis, H. (2022). An intelligent machine learning based routing scheme for VANET. IEEe Access, 10, 74318-74333.

Mengistu, F. G., & Yihunie, H. D. (2024). Performance analysis of path selection routing protocol for UVANET based on geographical multicast routing algorithm. Cogent Engineering, 11(1), 2311526.

Tangade, S., Manvi, S. S., & Lorenz, P. (2020). Trust management scheme based on hybrid cryptography for secure communications in VANETs. IEEE Transactions on Vehicular Technology, 69(5), 5232-5243.

Nazat, S., Li, L., & Abdallah, M. (2024). XAI-ADS: An explainable artificial intelligence framework for enhancing anomaly detection in autonomous driving systems. Ieee Access.

Vijayalakshmi, S., Bose, S., Logeswari, G., & Maheswaran, N. (2025). Smart parking: intelligent intrusion detection system in VANET enabled car parking system. Automatika, 66(2), 281-299.

Wang, W., Huang, H., Yin, Z., Gadekallu, T. R., Alazab, M., & Su, C. (2023). Smart contract token-based privacy-preserving access control system for industrial Internet of Things. Digital Communications and Networks, 9(2), 337-346.

Kazemi, M. M. K., Nabavi, Z., & Armaghani, D. J. (2024). A novel hybrid XGBoost methodology in predicting penetration rate of rotary based on rock-mass and material properties. Arabian Journal for Science and Engineering, 49(4), 5225-5241.

Keleko, A. T., Kamsu-Foguem, B., Ngouna, R. H., & Tongne, A. (2023). Health condition monitoring of a complex hydraulic system using Deep Neural Network and DeepSHAP explainable XAI. Advances in Engineering Software, 175, 103339.

Chen, Y. R., Rezapour, A., Tzeng, W. G., & Tsai, S. C. (2020). RL-routing: An SDN routing algorithm based on deep reinforcement learning. IEEE Transactions on Network Science and Engineering, 7(4), 3185-3199.

Dataset1:Python Developer [12-06-2025] (kaggle): [https://www.kaggle.com/datasets/programmer3/vanet-threat-dataset] Accessed on 12-06-2025

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Published

2025-06-23

How to Cite

1.
Akhther. A P, Maryposonia A, V. S P. Deep Reinforcement Learning Routing Engine and Threat Detection in Vehicular Networks through Federated Intelligence and Blockchain-Based Trust Ledger. J Neonatal Surg [Internet]. 2025Jun.23 [cited 2025Jul.20];14(32S). Available from: https://jneonatalsurg.com/index.php/jns/article/view/7497

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

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Copyright (c) 2025 Parveen Akhther. A, A. Maryposonia, Prasanth. V. S

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