can-train-and-test

can-train-and-test<b>This repository provides controller area network (CAN) datasets for the training and testing of machine learning schemes. The datasets are derived from the</b><b> can-dataset </b><b>and can-ml </b><b>repositories.</b>This repository contains controller area network (CAN) traffic for the 2017 Subaru Forester, the 2016 Chevrolet Silverado, the 2011 Chevrolet Traverse, and the 2011 Chevrolet Impala.For each vehicle, there are samples of attack-free traffic--that is, normal traffic--as well as samples of various types of attacks.The samples are stored in comma-separated values (CSV) format. All of the samples are labeled; attack frames are assigned "1," while attack-free frames are designated "0."This repository has been curated into four sub-datasets, dubbed "set_01," "set_02," "set_03," and "set_04." For each sub-dataset, there are five subsets: one training subset and four testing subsets. Each subset contains both attack-free and attack data.Training/testing subsets:<b>train_01:</b> Train the model<b>test_01_known_vehicle_known_attack:</b> Test the model against a known vehicle (seen in training) and known attacks (seen in training)<b>test_02_unknown_vehicle_known_attack:</b> Test the model against an unknown vehicle (not seen in training) and known attacks (seen in training)<b>test_03_known_vehicle_unknown_attack:</b> Test the model against a known vehicle (seen in training) and unknown attacks (not seen in training)<b>test_04_unknown_vehicle_unknown_attack:</b> Test the model against an unknown vehicle (not seen in training) and unknown attacks (not seen in training)The known/unknown attacks are identified by the file names (e.g., DoS, fuzzing, etc.). The known/unknown vehicles are as follows:<b>set_01</b>known vehicle --- Chevrolet Impalaunknown vehicle --- Chevrolet Silverado<b>set_02</b>known vehicle --- Chevrolet Traverseunknown vehicle --- Subaru Forester<b>set_03</b>known vehicle --- Chevrolet Silveradounknown vehicle --- Subaru Forester<b>set_04</b>known vehicle --- Subaru Foresterunknown vehicle --- Chevrolet Traverse本仓库（can-train-and-test）提供了用于机器学习模型训练与测试的控制器局域网（Controller Area Network, CAN）数据集。本数据集源自`can-dataset`与`can-ml`两个开源仓库。 本仓库包含四款车型的CAN总线流量数据，分别为2017款斯巴鲁森林人（Subaru Forester）、2016款雪佛兰索罗德（Chevrolet Silverado）、2011款雪佛兰巡领者（Chevrolet Traverse）以及2011款雪佛兰英帕拉（Chevrolet Impala）。 针对每款车型，数据集均包含无攻击流量（即正常流量）与多种类型的攻击流量样本。所有样本均以逗号分隔值（Comma-Separated Values, CSV）格式存储，且均已标注：攻击帧标记为"1"，无攻击帧标记为"0"。 本仓库已整理为四个子数据集，分别命名为"set_01"、"set_02"、"set_03"与"set_04"。每个子数据集又包含五个子集：一个训练子集与四个测试子集，每个子集均同时包含无攻击与攻击数据。 训练/测试子集说明如下： - train_01：用于模型训练 - test_01_known_vehicle_known_attack：使用训练集中出现过的已知车型与已知攻击对模型进行测试 - test_02_unknown_vehicle_known_attack：使用训练集中未出现过的未知车型与已知攻击对模型进行测试 - test_03_known_vehicle_unknown_attack：使用训练集中出现过的已知车型与未出现过的未知攻击对模型进行测试 - test_04_unknown_vehicle_unknown_attack：使用训练集中未出现过的未知车型与未知攻击对模型进行测试 已知/未知攻击可通过文件名（如DoS、模糊测试（Fuzzing）等）进行区分。已知/未知车型的对应关系如下： - set_01：已知车型为雪佛兰英帕拉，未知车型为雪佛兰索罗德 - set_02：已知车型为雪佛兰巡领者，未知车型为斯巴鲁森林人 - set_03：已知车型为雪佛兰索罗德，未知车型为斯巴鲁森林人 - set_04：已知车型为斯巴鲁森林人，未知车型为雪佛兰巡领者