# CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data

## TL;DR

CANet is an innovative unsupervised neural network designed to detect both known and unknown intrusions in high-dimensional CAN bus data, operating in real-time on individual messages with different IDs.

## Contribution

It introduces the first deep learning-based IDS that evaluates individual CAN messages in real-time, handling messages with varying IDs and frequencies.

## Key findings

- Outperforms previous machine learning methods significantly
- Effective on real and synthetic CAN data
- First to evaluate messages with different IDs in real-time

## Abstract

We propose a novel neural network architecture for detecting intrusions on the CAN bus. The Controller Area Network (CAN) is the standard communication method between the Electronic Control Units (ECUs) of automobiles. However, CAN lacks security mechanisms and it has recently been shown that it can be attacked remotely. Hence, it is desirable to monitor CAN traffic to detect intrusions. In order to detect both, known and unknown intrusion scenarios, we consider a novel unsupervised learning approach which we call CANet. To our knowledge, this is the first deep learning based intrusion detection system (IDS) that takes individual CAN messages with different IDs and evaluates them in the moment they are received. This is a significant advancement because messages with different IDs are typically sent at different times and with different frequencies. Our method is evaluated on real and synthetic CAN data. For reproducibility of the method, our synthetic data is publicly available. A comparison with previous machine learning based methods shows that CANet outperforms them by a significant margin.