LiPar

LiPar is a lightweight parallel learning model for practical in-vehicle network intrusion detection. LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security.

You can see the details in our paper LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection. (arXiv:2311.08000)

The Dataset

The dataset we used is Car-Hacking Dataset (you can fine the details of this dataset by the link). We also upload the data files in ./OriginalDataset/. Due to the limitation of upload file size, we compressed each data file into a .rar file. You can get the original data by unzipping the files.

The Data Processing

The codes for data processing are uploaded in ./DataProcessing/. Here are the steps for our data processing:

1. Data preprocessing and data cleaning: data_process.py is used to process attack datasets, including DoS_Attack_dataset, Fuzzy_Attack_dataset, Spoofing_the_RPM_gauge_dataset and Spoofing_the_drie_gear_dataset. data_process_normal.py is only used to process normal dataset. You can change the file_path and the new file name in df.to_csv function in the code to preprocess the datasets one by one. Then, it will generate five preprocessed data files respectively. The generated datasets can be found in the compressed file ./DataProcessing/PreprocessedData.rar.
2. Image data generating: img_generator_seq.py is used to process one-dimensional data sequentially into RGB image data. For attack datasets, there are both attack messages and normal messages. So, if an image is composed entirely of normal messages, we will label it as normal image. Otherwise, we will label it as attack image. Therefore, each attack dataset can generate two sets of image. You can set different directory addresses in image_path_attack and image_path_normal to store the generated normal and attack images. For each set of new generated images, the images will be named from 1 to n (n is the total number of images in the set) by sequence. When you finish processing one data file, you can change the filename and path in file_path to process other data files. The files we used are the preprocessed data files obtained in the previous step. For normal dataset, certainly, the program will only generate one set of normal image. At the end, you will obtain 9 sets of images in different directories.
3. Dataset partitioning: The directory we used to store all images is ./data_sequential_img/train/. Then, we need to divide the train set, validation set and the test set from all the image data. split_trainset.py is used to divide 30% of the total image data into validation and test set, the directory of which named ./data_sequential_img/val/. Futhermore, split_testset.py is used to divide $\frac{1}{3}$ of the images in val set into test set named ./data_sequential_img/test/. Finally, the ratio of images in the training set, validation set, and test set is 7:2:1. Also, you can change the path and directory name to anything you want by modifying Train_Dir, Val_Dir and Test_Dir.