Unsupervised Anomaly Detection for Automotive CAN Bus on the Intel Loihi

Detecting anomalies and faults swiftly and accurately is essential in many applications, such as healthcare, infrastructure, industry, and security. In most of these applications, the available power for running an anomaly detection system is very limited. This is especially the case for modern smart and electric vehicles, which have an increasing amount of electronics. These vehicles have Electronic Control Units (ECUs) that communicate with each other through the Controller Area Network (CAN) bus. The Controller Area Network, however, is not very secure, so bad actors can do severe damage, putting the lives of drivers and passengers at risk. Thus, low-power anomaly detection is essential for both security and reliability. Deep learning networks have demonstrated proficiency in anomaly identification. However, their implementation on conventional Central Processing Units (CPUs) and Graphics Processing Units (GPUs) incurs significant energy consumption. To overcome this limitation, we propose a low-power approach for placing security directly in the network hardware. An autoencoder-based real-time anomaly detector trained through unsupervised learning is seamlessly mapped onto the Intel Loihi spiking neuromorphic processor. Our proposed anomaly detector used significantly less power than a CPU or GPU and showed the best accuracy and F-1 score to detect anomalies compared to the alternative spiking approaches. The proposed anomaly detection system consumed about 20,000 and 700 times less energy than a CPU and GPU respectively. To the best of our knowledge, this is the first low-power, unsupervised anomaly detection system using the Loihi or any other neuromorphic processor. The results of this work will apply to other anomaly detection applications, as well as other spiking neuromorphic processors, resulting in a universally applicable extreme low-power anomaly detection platform.