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1.国家计算机网络应急技术处理协调中心,北京 100029
2.国家网信办数据与技术保障中心,北京 100080
3.中国电信股份有限公司江苏分公司,江苏 南京 210007
4.北京邮电大学网络空间安全学院,北京 100876
[ "秘蓉新(1984- ),女,国家计算机网络应急技术处理协调中心副研究员,主要研究方向为人工智能多模态识别、网络安全、信息安全。" ]
[ "林志强(1984- ),男,现就职于国家网信办数据与技术保障中心,主要研究方向为网络数据安全、个人信息保护。" ]
[ "齐佳豪(1999- ),男,现就职于中国电信股份有限公司江苏分公司,主要研究方向为信息安全认证评估、网络安全态势感知。" ]
[ "姚文文(1984- ),女,国家计算机网络应急技术处理协调中心助理研究员,主要研究方向为网络信息安全战略、信息科技发展态势。" ]
[ "左金鑫(1992- ),女,北京邮电大学网络空间安全学院在站博士后,主要研究方向为车联网安全、网络安全风险评估。" ]
[ "陆月明(1969- ),男,博士,北京邮电大学网络空间安全学院教授,主要研究方向为车联网安全、网络安全风险评估。" ]
收稿日期:2024-07-19,
修回日期:2024-10-14,
纸质出版日期:2024-11-20
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秘蓉新,林志强,齐佳豪等.基于动态权重分配的智能汽车网络安全评估模型[J].电信科学,2024,40(11):79-90.
MI Rongxin,LIN Zhiqiang,QI Jiahao,et al.Intelligent automotive network security evaluation model based on dynamic weight allocation[J].Telecommunications Science,2024,40(11):79-90.
秘蓉新,林志强,齐佳豪等.基于动态权重分配的智能汽车网络安全评估模型[J].电信科学,2024,40(11):79-90. DOI: 10.11959/j.issn.1000-0801.2024239.
MI Rongxin,LIN Zhiqiang,QI Jiahao,et al.Intelligent automotive network security evaluation model based on dynamic weight allocation[J].Telecommunications Science,2024,40(11):79-90. DOI: 10.11959/j.issn.1000-0801.2024239.
评估指标权重的确定是影响智能汽车网络安全性评估的重要因素之一。针对传统确权方法忽略指标属性状态变化对评估指标权重影响的问题,提出了一种基于动态权重分配的网络安全评估模型。该模型首先对车辆自组织网络(vehicular Ad Hoc network,VANET)进行安全目标分解与分析,构建其安全性评估指标体系。针对构建出的安全性评估指标体系,利用基于排序的确权算法对安全指标进行指标关联性分析,随后采用所提出的动态权重分配算法,计算指标体系中各个指标的动态权重,进而实现智能汽车VANET的安全性评估,得到安全等级评估结果。实验结果表明,该模型可以提升智能汽车VANET评估的合理性。
The determination of the weight of evaluation indicators is one of the important factors affecting the security evaluation of intelligent vehicle networks. A network security evaluation model based on dynamic weight allocation was proposesd to address the problem of traditional property rights confirmation methods ignoring the impact of changes in indicator attribute states on the weight of evaluation indicators. The model first decomposed and analyzed the security objectives of the vehicle Ad Hoc network (VANET)
and constructed its security evaluation index system. Based on the constructed security evaluation index system
the correlation analysis of security indicators was carried out using a sorting and confirmation algorithm. Then
the proposed dynamic weight allocation algorithm was used to calculate the dynamic weights of each indicator in the index system
thereby achieving the security evaluation of the intelligent vehicle VANET and obtaining the security level evaluation results. The experimental results indicate that the model can improve the rationality of intelligent vehicle VANET evaluation.
GUAN T , HAN Y , KANG N , et al . An overview of vehicular cybersecurity for intelligent connected vehicles [J ] . Sustainability , 2022 , 14 ( 9 ): 5211 .
ZHAO H , GUO J , WU Z , et al . Cyber security risk analysis and evaluation for intelligent vehicle gateway [C ] // Proceedings of the International Conference on Smart Transportation and City Engineering 2021 . SPIE , 2021 , 12050 : 662 - 670 .
WANG K , ZHANG A , SUN H , et al . Analysis of recent deep-learning-based intrusion detection methods for in-vehicle network [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 24 ( 2 ): 1843 - 1854 .
KALKAN S C , SAHINGOZ O K . In-vehicle intrusion detection system on controller area network with machine learning models [C ] // Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) . Piscataway : IEEE Press , 2020 : 1 - 6 .
ALFARDUS A , RAWAT D B . Intrusion detection system for CAN bus in-vehicle network based on machine learning algorithms [C ] // Proceedings of the 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) . Piscataway : IEEE Press , 2021 : 944 - 949 .
LOKMAN S F , OTHMAN A T , ABU-BAKAR M H . Intrusion detection system for automotive controller Area Network (CAN) bus system: a review [J ] . EURASIP Journal on Wireless Communications and Networking , 2019 ( 1 ): 184 .
BANAFSHEHVARAGH S T , RAHMANI A M . Intrusion, anomaly, and attack detection in smart vehicles [J ] . Microprocessors and Microsystems , 2023 ( 96 ): 104726 .
MALIK R Q , ALSATTAR H A , RAMLI K N , et al . Mapping and deep analysis of vehicle-to-infrastructure communication systems: coherent taxonomy, datasets, evaluation and performance measurements, motivations, open challenges, recommendations, and methodological aspects [J ] . IEEE Access , 2019 ( 7 ): 126753 - 126772 .
ALI AMEEN H , MAHAMAD A K , ZAIDAN B B , et al . A deep review and analysis of data exchange in vehicle-to-vehicle communications systems: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions [J ] . IEEE Access , 2019 ( 7 ): 158349 - 158378 .
KELARESTAGHI K B , FORUHANDEH M , HEASLIP K , et al . Intelligent transportation system security: impact-oriented risk assessment of in-vehicle networks [J ] . IEEE Intelligent Transportation Systems Magazine , 2019 , 13 ( 2 ): 91 - 104 .
SHAO X , DONG C , DONG L . Research on detection and evaluation technology of cybersecurity in intelligent and connected vehicle [C ] // Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) . Piscataway : IEEE Press , 2019 : 413 - 416 .
KONG H K , HONG M K , KIM T S . Security risk assessment framework for smart car using the attack tree analysis [J ] . Journal of Ambient Intelligence and Humanized Computing , 2018 ( 9 ): 531 - 551 .
LI Q , ZUO J X , CAO R H , et al . A security evaluation framework for intelligent connected vehicles based on attack chains [J ] . IEEE Network , 2023 , 38 ( 2 ): 148 - 155 .
于海洋 , 陈秀真 , 马进 , 等 . 面向智能汽车的信息安全漏洞评分模型 [J ] . 网络与信息安全学报 , 2022 , 8 ( 1 ): 167 - 179 .
YU H Y , CHEN X Z , MA J , et al . Information security vulnerability scoring model for intelligent vehicles [J ] . Chinese Journal of Network and Information Security , 2022 , 8 ( 1 ): 167 - 179 .
杨雪婷 , 李重 . 车联网中基于车辆行为预测的身份认证方案 [J ] . 计算机工程 , 2021 , 47 ( 1 ): 129 - 138 .
YANG X T , LI Z . Identity authentication scheme based on vehicle behavior prediction for IoV [J ] . Computer Engineering , 2021 , 47 ( 1 ): 129 - 138 .
于赫 . 网联汽车信息安全问题及CAN总线异常检测技术研究 [D ] . 长春 : 吉林大学 , 2016 .
YU H . Research on information security of networked automobile and CAN bus anomaly detection technology [D ] . Changchun : Jilin University , 2016 .
李飞 , 王超 . 基于关联规则挖掘的车载网络入侵检测技术研究 [J ] . 数据挖掘 , 2017 , 7 ( 3 ): 65 - 69 .
LI F , WANG C . Research on intrusion detection technology based on association rules mining in vehicular networks [J ] . Hans Journal of Data Mining , 2017 , 7 ( 3 ): 65 - 69 .
李洪兴 . 因素空间理论与知识表示的数学框架(Ⅷ): 变权综合原理 [J ] . 模糊系统与数学 , 1995 , 9 ( 3 ): 1 - 9 .
LI H X . Factor spaces and mathematical frame of knowledge representation(Ⅷ)──Variable weights analysis [J ] . Fuzzy Systems and Mathematics , 1995 , 9 ( 3 ): 1 - 9 .
李洪兴 . 因素空间理论与知识表示的数学框架(Ⅸ): 均衡函数的构造与Weber-Fechner特性 [J ] . 模糊系统与数学 , 1996 , 10 ( 3 ): 12 - 17, 19 .
LI H X . Factor space theory and mathematical framework of knowledge representation (Ⅸ) ── construction of equilibrium function and Weber-Fechner characteristics [J ] . Fuzzy Systems and Mathematics , 1996 , 10 ( 3 ): 12 - 17, 19 .
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