Empirical-Evaluation-Of-In-Vehicle-IntrusionDetection-System-using-Deep-Learning (The code will be available after the proceeding announcemnt)
Modern and future vehicles are complex cyber-physical sys-tems. The connection to their outside environment raises many securityproblems that impact our safety directly. In this work, we propose a DeepCAN intrusion detection system framework. We propose a multivariatetime series representation for asynchronous CAN data. This represen-tation enhances the temporal modelling of deep learning architecturesfor anomaly detection. We study different deep learning tasks (super-vised/unsupervised) and compare different architectures, to propose anin-vehicle intrusion detection system that fits constraints of memory andcomputational power of the in-vehicle system. The proposed intrusiondetection system is time window wise: any given time frame is labelledeither anomalous or normal. We conduct experiments with many types ofattacks on an in-vehicle CAN using SynCAn dataset. We show that oursystem yields good results and allow to detect different kinds of attacks.
DATAset
The dataset is available at : https://github.com/etas/SynCAN.
Note that, u need to set the configuration of the data set that u wish to have (create the train and test data). and change the variable "root_dir" in maint.py.
Prerequisites
tensorflow 2.0. keras.
Running Classification
python main.py (name of the folder containing all the TS datasets) (specific dataset containing train, test raws) classification_model_name _itr_1 True
Running Encoders
python main.py (name of the folder containing all the TS datasets) (specific dataset containing train, test raws) encoder_model_name _itr_1 True