网络与信息安全学报 ›› 2022, Vol. 8 ›› Issue (1): 167-179.doi: 10.11959/j.issn.2096-109x.2021096

• 学术论文 • 上一篇    下一篇

面向智能汽车的信息安全漏洞评分模型

于海洋1,2, 陈秀真1,2, 马进1,2, 周志洪1,2, 侯书凝1,2   

  1. 1 上海交通大学网络安全技术研究院，上海 200240
    2 上海市信息安全综合管理技术重点实验室，上海 200240
  • 修回日期:2021-08-12 出版日期:2022-02-15 发布日期:2022-02-01
  • 作者简介:于海洋（1996− ），男，山东泰安人，上海交通大学硕士生，主要研究方向为车联网信息安全、网络安全
    陈秀真（1977− ），女，山东聊城人，博士，上海交通大学副教授，主要研究方向为车联网信息安全、网络信息系统安全检测与评估、社交网络大数据分析
    马进（1977− ），女，山东滕州人，博士，上海交通大学高级工程师，主要研究方向为大数据与人工智能应用、车联网信息安全、网络空间安全综合管理新技术
    周志洪（1979− ），男，江西九江人，上海交通大学讲师，主要研究方向为密码应用、安全测评、车联网安全
    侯书凝（1998− ），女，江西抚州人，上海交通大学硕士生，主要研究方向为车联网信息安全
  • 基金资助:
    国家自然科学基金联合基金(U2003206);上海市工业强基专项(GYQJ-2018-3-03)

Information security vulnerability scoring model for intelligent vehicles

Haiyang YU1,2, Xiuzhen CHEN1,2, Jin MA1,2, Zhihong ZHOU1,2, Shuning HOU1,2   

  1. 1 Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai 200240, China
    2 Shanghai Municipal Key Lab of Integrated Management Technology for Information Security, Shanghai 200240, China
  • Revised:2021-08-12 Online:2022-02-15 Published:2022-02-01
  • Supported by:
    The Joint Funds of the National Natural Science Foundation of China(U2003206);Shanghai industrial foundation project(GYQJ-2018-3-03)

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摘要：

随着汽车智能化、网联化的发展，汽车中集成了越来越多的电子器件，数量庞大的硬件、固件和软件中隐藏着各种设计缺陷和漏洞，这从根本上导致了智能汽车信息安全问题。大量汽车漏洞的披露，严重影响了汽车安全，制约了智能汽车的广泛应用。漏洞管理是降低漏洞危害、改善汽车安全的有效手段。在漏洞管理流程中，漏洞评估是决定漏洞处置优先级的重要一环。但是，现有的漏洞评分系统不能合理地评估智能汽车安全漏洞。为了解决智能汽车漏洞评估不合理的问题，提出面向智能汽车的信息安全漏洞评分模型。基于通用漏洞评分系统（CVSS）漏洞评分原理，根据智能汽车的特点，优化了 CVSS 的攻击向量和攻击复杂度，并添加了财产安全、隐私安全、功能安全和生命安全4个指标来刻画漏洞可能对智能汽车造成的影响；结合机器学习的方法，对CVSS评分公式参数进行了调整，以使其更好地刻画智能汽车信息安全漏洞特点，适应调整后的指标权重。通过实例评估和统计系统特征分布发现，模型拥有更好的多样性和更稳定连续的特征分布，表明模型可以更好地对不同漏洞进行评分；并且基于模型评估得到的漏洞评分，应用层次分析法给出整车脆弱性评估，表征整车风险水平。所提模型相比现有模型可以更为合理地评价智能汽车中信息安全漏洞的严重程度，科学地评估整车或者部分系统的安全风险，为汽车漏洞的修复与加固提供依据。

关键词: 智能汽车, 通用漏洞评分系统, 漏洞评分, 风险评估, 非线性回归, 层次分析法

Abstract:

More and more electronic devices are integrated into the modern vehicles with the development of intelligent vehicles.There are various design flaws and vulnerabilities hidden in a large number of hardware, firmware and software.Therefore, the vulnerabilities of intelligent vehicles have become the most important factor affecting the vehicle safety.The safety of vehicles is seriously affected by the disclosure of a large number of vulnerabilities, and the wide application of smart cars is also restricted.Vulnerability management is an effective method to reduce the risk of vulnerabilities and improve vehicle security.And vulnerability scoring is one the important step in vulnerability management procedure.However, current method have no capability assessing automotive vulnerabilities reasonably.In order to handle this problem, a vulnerability scoring model for intelligent vehicles was proposed, which was based on CVSS.The attack vector and attack complexity were optimized, and property security, privacy security, functional safety and life safety were added to characterize the possible impact of the vulnerabilities according to the characteristics of intelligent vehicles.With the machine learning method, the parameters in CVSS scoring formula were optimized to describe the characteristics of intelligent vehicle vulnerabilities and adapt to the adjusted and new added weights.It is found in case study and statistics that the diversity and distribution of the model are better than CVSS, which means the model can better score different vulnerabilities.And then AHP is used to evaluate the vulnerability of the whole vehicle based on the vulnerability score of the model, a score is given representing the risk level of whole vehicle.The proposed model can be used to evaluate the severity of information security vulnerabilities in intelligent vehicles and assess the security risks of the entire vehicle or part of the system reasonably, which can provide an evidence for fixing the vulnerabilities or reinforcing the entire vehicle.

Key words: intelligent vehicle, CVSS, vulnerability scoring system, risk assessment, nonlinear regression, AHP

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