基于SA-BiLSTM和模同态加密增强深度RL路由的VANET安全数据传输入侵检测

IF 0.8 Q4 OPTICS
T. Pavithra, B. S. Nagabhushana
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引用次数: 0

摘要

车载自组织网络(VANET)已成为一项革命性的创新技术,是智能交通系统(ITS)的重要组成部分。然而,由于其无线特性和复杂的操作环境,vanet很容易受到一系列恶意用户的攻击。为了在所有系统车辆之间提供可靠和安全的通信,识别VANET系统中的入侵是至关重要的。由于缺乏数据、可解释性和类的不平衡等限制,传统方法不再有效。因此,该方法利用SA-BiLSTM开发了一种增强的深度RL路由(EDRL)来检测入侵,并使用模块化同态加密创建了一个安全的VANET系统。在该模型中,考虑道路上是否发生事故,使用改进的K谐波均值聚类算法(IKHM)对该路段的车辆进行分组,并使用大蔗鼠算法(GCRA)优化,根据其最小距离和最高能量确定CH。然后使用EDRL路由技术将数据交换给RSU以选择合适的路由。RSU使用基于自我注意的双向长短期记忆(SA-BiLSTM)分类器发现了攻击和非攻击的不同类型。然后使用模态同态加密(ModHE)对非攻击数据进行编码,并将其上传到云端,将警告信息传递给车载网络。对该模型的性能参数进行了测试,结果表明,对于500个车辆节点,PDR为82.2%,能耗为13.65J,路由开销为20.3%,吞吐量为18.7 mbps,延迟为11.22。攻击检测的准确率、命中率和PPV分别为96.3、96.7和95.8%。此外,执行时间和加密时间分别为16.63毫秒和46.03毫秒。上述结果表明,所提出的框架在提供非常节能和安全的V2X通信网络方面优于先前的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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.

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来源期刊
CiteScore
1.50
自引率
11.10%
发文量
25
期刊介绍: The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
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