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Abstract <p>Vehicular Ad Hoc Network (VANET) has become a revolutionary and creative technology that serves as an essential part of Intelligent Transportation Systems (ITS). However, due to their wireless nature and complex operating environment, VANETs are vulnerable to a range of malicious user assaults. It is critical to identify intrusions in the VANET system in order to provide reliable and secure communication among all of the system’s vehicles. Traditional methods are no longer effective due to some limitations like lack of data, interpretability and imbalance classes. Therefore, the proposed approach developed an enhanced deep RL routing (EDRL) with SA-BiLSTM for the detection of intrusion and created a secure VANET system employing modular Homomorphic encryption. In this proposed model, consider if any incident happens on the road, vehicles in that sector are grouped by utilizing the Improved K harmonic means clustering algorithm (IKHM), and the CH is determined according to its minimal distance and highest energy using the Greater Cane Rat Algorithm (GCRA) optimization. The EDRL routing technique is then used to exchange the data to RSU for choosing the appropriate route. RSU discovered the different types of attack and non-attack using Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) classifier. Then the non-attack data are encoded using the Modular Homomorphic Encryption (ModHE) and uploaded in the cloud to intimate the warning message to the vehicular networks. The proposed model’s performance parameters are examined, and the results show that, for 500 vehicle nodes, the outcomes are 82.2% PDR, 13.65J energy usage, 20.3% routing overhead, 18.7 mbps throughput, and 11.22 delay. Accuracy, hit rate, and PPV are assessed at 96.3, 96.7, and 95.8%, respectively, for attack detection. Furthermore, the execution time and encryption take 16.63 and 46.03 milliseconds, respectively. The mentioned results demonstrated that the proposed framework outperformed earlier methods in providing a remarkably energy-efficient as well as secure V2X communication network.</p>

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Intrusion Detection Using SA-BiLSTM and Enhanced Deep RL Routing with Modular Homomorphic Encryption for Secure Data Transmission in VANET

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • T. Pavithra,
  • B. S. Nagabhushana

摘要

Abstract

Vehicular Ad Hoc Network (VANET) has become a revolutionary and creative technology that serves as an essential part of Intelligent Transportation Systems (ITS). However, due to their wireless nature and complex operating environment, VANETs are vulnerable to a range of malicious user assaults. It is critical to identify intrusions in the VANET system in order to provide reliable and secure communication among all of the system’s vehicles. Traditional methods are no longer effective due to some limitations like lack of data, interpretability and imbalance classes. Therefore, the proposed approach developed an enhanced deep RL routing (EDRL) with SA-BiLSTM for the detection of intrusion and created a secure VANET system employing modular Homomorphic encryption. In this proposed model, consider if any incident happens on the road, vehicles in that sector are grouped by utilizing the Improved K harmonic means clustering algorithm (IKHM), and the CH is determined according to its minimal distance and highest energy using the Greater Cane Rat Algorithm (GCRA) optimization. The EDRL routing technique is then used to exchange the data to RSU for choosing the appropriate route. RSU discovered the different types of attack and non-attack using Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) classifier. Then the non-attack data are encoded using the Modular Homomorphic Encryption (ModHE) and uploaded in the cloud to intimate the warning message to the vehicular networks. The proposed model’s performance parameters are examined, and the results show that, for 500 vehicle nodes, the outcomes are 82.2% PDR, 13.65J energy usage, 20.3% routing overhead, 18.7 mbps throughput, and 11.22 delay. Accuracy, hit rate, and PPV are assessed at 96.3, 96.7, and 95.8%, respectively, for attack detection. Furthermore, the execution time and encryption take 16.63 and 46.03 milliseconds, respectively. The mentioned results demonstrated that the proposed framework outperformed earlier methods in providing a remarkably energy-efficient as well as secure V2X communication network.