CAN入侵检测数据综合指南与ROAD数据集介绍
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset
AI总结：
本文首次系统梳理现有CAN入侵数据集并指出其局限，提出包含3.5小时真实车辆数据、多种隐蔽攻击的ROAD数据集，以促进CAN入侵检测的基准测试与结果可比性。
摘要：
尽管控制器局域网（CAN）在现代车辆中无处不在，但其缺乏基本的安全属性，容易被利用。一个快速发展的CAN安全研究领域已经出现，旨在检测CAN上的入侵。对于大多数研究人员来说，生成包含各种入侵的车辆CAN数据是遥不可及的，因为这需要昂贵的设备和专业知识。为了帮助研究人员，我们提供了首个关于现有开放CAN入侵数据集的综合指南，包括对每个数据集的质量分析，以及每个数据集的优点、缺点和建议使用场景的列举。当前的公共CAN入侵检测系统（IDS）数据集仅限于真实的伪造（简单消息注入）攻击和通常在合成数据中的模拟攻击，这些数据缺乏保真度。总的来说，攻击对车辆的实际物理影响在可用数据集中未得到验证。只有一个数据集提供了信号转换后的数据，但没有相应的原始二进制版本。总体而言，可用数据将CAN IDS工作局限于在有限且往往不合适的数据上进行测试（通常攻击过于容易被检测，无法真正测试方法），这种数据缺乏阻碍了结果的可比性和可复现性。作为我们的主要贡献，我们提出了ROAD（真实橡树岭国家实验室汽车测功机）CAN入侵数据集，包含一辆车超过3.5小时的CAN数据。ROAD包含在各种活动期间记录的背景数据，以及具有多种变体和实例的日益隐蔽的攻击，包括真实的模糊测试、伪造和独特的高级攻击，以及模拟的伪装攻击。为了便于对需要信号转换输入的CAN IDS方法进行基准测试，我们还提供了许多CAN捕获的信号时间序列格式。我们的贡献旨在促进CAN IDS领域适当的基准测试和所需的可比性。
英文摘要：
Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions on CANs. Producing vehicular CAN data with a variety of intrusions is out of reach for most researchers as it requires expensive assets and expertise. To assist researchers, we present the first comprehensive guide to the existing open CAN intrusion datasets, including a quality analysis of each dataset and an enumeration of each's benefits, drawbacks, and suggested use case. Current public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, which lack fidelity. In general, the physical effects of attacks on the vehicle are not verified in the available datasets. Only one dataset provides signal-translated data but not a corresponding raw binary version. Overall, the available data pigeon-holes CAN IDS works into testing on limited, often inappropriate data (usually with attacks that are too easily detectable to truly test the method), and this lack data has stymied comparability and reproducibility of results. As our primary contribution, we present the ROAD (Real ORNL Automotive Dynamometer) CAN Intrusion Dataset, consisting of over 3.5 hours of one vehicle's CAN data. ROAD contains ambient data recorded during a diverse set of activities, and attacks of increasing stealth with multiple variants and instances of real fuzzing, fabrication, and unique advanced attacks, as well as simulated masquerade attacks. To facilitate benchmarking CAN IDS methods that require signal-translated inputs, we also provide the signal time series format for many of the CAN captures. Our contributions aim to facilitate appropriate benchmarking and needed comparability in the CAN IDS field.
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