can-train-and-test

can-train-and-testThis repository provides controller area network (CAN) datasets for the training and testing of machine learning schemes. The datasets are derived from the can-dataset and can-ml repositories.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:train_01: Train the modeltest_01_known_vehicle_known_attack: Test the model against a known vehicle (seen in training) and known attacks (seen in training)test_02_unknown_vehicle_known_attack: Test the model against an unknown vehicle (not seen in training) and known attacks (seen in training)test_03_known_vehicle_unknown_attack: Test the model against a known vehicle (seen in training) and unknown attacks (not seen in training)test_04_unknown_vehicle_unknown_attack: 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:set_01known vehicle --- Chevrolet Impalaunknown vehicle --- Chevrolet Silveradoset_02known vehicle --- Chevrolet Traverseunknown vehicle --- Subaru Foresterset_03known vehicle --- Chevrolet Silveradounknown vehicle --- Subaru Foresterset_04known vehicle --- Subaru Foresterunknown vehicle --- Chevrolet Traverse本仓库（can-train-and-test）提供用于机器学习方案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两个代码仓库。 本仓库包含四款车型的控制器局域网通信流量数据，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，数据集均涵盖无攻击通信流量（即正常通信流量）样本与各类攻击流量样本。 所有样本均以逗号分隔值（Comma-Separated Values, CSV）格式存储，且带有标注：攻击帧标注为"1"，无攻击帧标注为"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。每个子数据集均包含五个子集：一个训练子集与四个测试子集，每个子集同时包含无攻击与攻击流量数据。 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类型对模型进行测试 test_02_unknown_vehicle_known_attack：使用训练集中未出现的未知车型与已知攻击类型对模型进行测试 test_03_known_vehicle_unknown_attack：使用训练集中出现过的已知车型与未知攻击类型对模型进行测试 test_04_unknown_vehicle_unknown_attack：使用训练集中未出现的未知车型与未知攻击类型对模型进行测试 已知/未知攻击类型可通过文件名识别（例如拒绝服务攻击（Denial of Service, DoS）、模糊测试（fuzzing）等）。已知/未知车型的划分规则如下： set_01：已知车型为雪佛兰英帕拉（Chevrolet Impala），未知车型为雪佛兰索罗德（Chevrolet Silverado） set_02：已知车型为雪佛兰巡领者（Chevrolet Traverse），未知车型为斯巴鲁森林人（Subaru Forester） set_03：已知车型为雪佛兰索罗德（Chevrolet Silverado），未知车型为斯巴鲁森林人（Subaru Forester） set_04：已知车型为斯巴鲁森林人（Subaru Forester），未知车型为雪佛兰巡领者（Chevrolet Traverse）