利用模糊散列生成快速高效的上下文感知嵌入,用于车载网络入侵检测

IF 5.8 2区 计算机科学 Q1 TELECOMMUNICATIONS
Moon Jeong Choi , Ik Rae Jeong , Hyun Min Song
{"title":"利用模糊散列生成快速高效的上下文感知嵌入,用于车载网络入侵检测","authors":"Moon Jeong Choi ,&nbsp;Ik Rae Jeong ,&nbsp;Hyun Min Song","doi":"10.1016/j.vehcom.2024.100786","DOIUrl":null,"url":null,"abstract":"<div><p>In the rapidly advancing field of automotive cybersecurity, the protection of In-Vehicle Networks (IVNs) against cyber threats is crucial. Current deep learning solutions offer robustness but at the cost of high computational demand and potential privacy breaches due to the extensive IVN data required for model training. Our study proposes a novel intrusion detection system (IDS) specifically designed for IVNs that prioritizes computational efficiency and data privacy. Utilizing fuzzy hashing techniques, we generate context-aware embeddings that effectively preserve the privacy of IVN data. Among the machine learning algorithms evaluated, the Support Vector Machine (SVM) emerged as the most effective, particularly when paired with TLSH hash embeddings. This combination achieved notable detection performance, as substantiated by T-SNE visualizations that demonstrate a distinct segregation of normal and attack traffic within the vector space. To validate the effectiveness and practicality of our proposed IDS, we conducted exhaustive experiments on the well-known car-hacking dataset and the more complex ROAD dataset, which includes diverse and sophisticated attack scenarios. Our findings reveal that the proposed lightweight IDS not only demonstrates high detection accuracy but also maintains this performance within the computational constraints of current IVN systems. The system's capability to operate effectively in real-time environments makes it a viable solution for modern automotive cybersecurity needs.</p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":null,"pages":null},"PeriodicalIF":5.8000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast and efficient context-aware embedding generation using fuzzy hashing for in-vehicle network intrusion detection\",\"authors\":\"Moon Jeong Choi ,&nbsp;Ik Rae Jeong ,&nbsp;Hyun Min Song\",\"doi\":\"10.1016/j.vehcom.2024.100786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In the rapidly advancing field of automotive cybersecurity, the protection of In-Vehicle Networks (IVNs) against cyber threats is crucial. Current deep learning solutions offer robustness but at the cost of high computational demand and potential privacy breaches due to the extensive IVN data required for model training. Our study proposes a novel intrusion detection system (IDS) specifically designed for IVNs that prioritizes computational efficiency and data privacy. Utilizing fuzzy hashing techniques, we generate context-aware embeddings that effectively preserve the privacy of IVN data. Among the machine learning algorithms evaluated, the Support Vector Machine (SVM) emerged as the most effective, particularly when paired with TLSH hash embeddings. This combination achieved notable detection performance, as substantiated by T-SNE visualizations that demonstrate a distinct segregation of normal and attack traffic within the vector space. To validate the effectiveness and practicality of our proposed IDS, we conducted exhaustive experiments on the well-known car-hacking dataset and the more complex ROAD dataset, which includes diverse and sophisticated attack scenarios. Our findings reveal that the proposed lightweight IDS not only demonstrates high detection accuracy but also maintains this performance within the computational constraints of current IVN systems. The system's capability to operate effectively in real-time environments makes it a viable solution for modern automotive cybersecurity needs.</p></div>\",\"PeriodicalId\":54346,\"journal\":{\"name\":\"Vehicular Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vehicular Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214209624000615\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vehicular Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214209624000615","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

在快速发展的汽车网络安全领域,保护车载网络(IVN)免受网络威胁至关重要。当前的深度学习解决方案具有鲁棒性,但代价是高计算需求和潜在的隐私泄露,因为模型训练需要大量的 IVN 数据。我们的研究提出了一种专为 IVN 设计的新型入侵检测系统(IDS),该系统优先考虑计算效率和数据隐私。利用模糊散列技术,我们生成了能有效保护 IVN 数据隐私的上下文感知嵌入。在所评估的机器学习算法中,支持向量机(SVM)是最有效的,尤其是与 TLSH 散列嵌入相结合时。这一组合取得了显著的检测性能,T-SNE 可视化效果证明了这一点,该效果显示了向量空间内正常流量和攻击流量的明显区分。为了验证我们提出的 IDS 的有效性和实用性,我们在著名的汽车黑客数据集和更复杂的 ROAD 数据集上进行了详尽的实验。我们的研究结果表明,所提出的轻量级 IDS 不仅具有很高的检测准确性,而且还能在当前 IVN 系统的计算限制条件下保持这种性能。该系统能在实时环境中有效运行,因此是满足现代汽车网络安全需求的可行解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast and efficient context-aware embedding generation using fuzzy hashing for in-vehicle network intrusion detection

In the rapidly advancing field of automotive cybersecurity, the protection of In-Vehicle Networks (IVNs) against cyber threats is crucial. Current deep learning solutions offer robustness but at the cost of high computational demand and potential privacy breaches due to the extensive IVN data required for model training. Our study proposes a novel intrusion detection system (IDS) specifically designed for IVNs that prioritizes computational efficiency and data privacy. Utilizing fuzzy hashing techniques, we generate context-aware embeddings that effectively preserve the privacy of IVN data. Among the machine learning algorithms evaluated, the Support Vector Machine (SVM) emerged as the most effective, particularly when paired with TLSH hash embeddings. This combination achieved notable detection performance, as substantiated by T-SNE visualizations that demonstrate a distinct segregation of normal and attack traffic within the vector space. To validate the effectiveness and practicality of our proposed IDS, we conducted exhaustive experiments on the well-known car-hacking dataset and the more complex ROAD dataset, which includes diverse and sophisticated attack scenarios. Our findings reveal that the proposed lightweight IDS not only demonstrates high detection accuracy but also maintains this performance within the computational constraints of current IVN systems. The system's capability to operate effectively in real-time environments makes it a viable solution for modern automotive cybersecurity needs.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Vehicular Communications
Vehicular Communications Engineering-Electrical and Electronic Engineering
CiteScore
12.70
自引率
10.40%
发文量
88
审稿时长
62 days
期刊介绍: Vehicular communications is a growing area of communications between vehicles and including roadside communication infrastructure. Advances in wireless communications are making possible sharing of information through real time communications between vehicles and infrastructure. This has led to applications to increase safety of vehicles and communication between passengers and the Internet. Standardization efforts on vehicular communication are also underway to make vehicular transportation safer, greener and easier. The aim of the journal is to publish high quality peer–reviewed papers in the area of vehicular communications. The scope encompasses all types of communications involving vehicles, including vehicle–to–vehicle and vehicle–to–infrastructure. The scope includes (but not limited to) the following topics related to vehicular communications: Vehicle to vehicle and vehicle to infrastructure communications Channel modelling, modulating and coding Congestion Control and scalability issues Protocol design, testing and verification Routing in vehicular networks Security issues and countermeasures Deployment and field testing Reducing energy consumption and enhancing safety of vehicles Wireless in–car networks Data collection and dissemination methods Mobility and handover issues Safety and driver assistance applications UAV Underwater communications Autonomous cooperative driving Social networks Internet of vehicles Standardization of protocols.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信