| Title | Rapid, Multi-vehicle and Feed-forward Neural Network based Intrusion Detection System for Controller Area Network Bus |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Sami, Muhammad, Ibarra, Matthew, Esparza, Anamaria C., Al-Jufout, Saleh, Aliasgari, Mehrdad, Mozumdar, Mohammad |
| Conference Name | 2020 IEEE Green Energy and Smart Systems Conference (IGESSC) |
| Date Published | nov |
| Keywords | automotive, Biological neural networks, controller area network, controller area network security, Cyber-physical systems, Deep Neural Network, feed-forward neural network, Internet of Things, Intrusion detection, Neurons, pubcrawl, Real-time Systems, Resiliency, Scalability, Testing, Training |
| Abstract | In this paper, an Intrusion Detection System (IDS) in the Controller Area Network (CAN) bus of modern vehicles has been proposed. NESLIDS is an anomaly detection algorithm based on the supervised Deep Neural Network (DNN) architecture that is designed to counter three critical attack categories: Denial-of-service (DoS), fuzzy, and impersonation attacks. Our research scope included modifying DNN parameters, e.g. number of hidden layer neurons, batch size, and activation functions according to how well it maximized detection accuracy and minimized the false positive rate (FPR) for these attacks. Our methodology consisted of collecting CAN Bus data from online and in real-time, injecting attack data after data collection, preprocessing in Python, training the DNN, and testing the model with different datasets. Results show that the proposed IDS effectively detects all attack types for both types of datasets. NESLIDS outperforms existing approaches in terms of accuracy, scalability, and low false alarm rates. |
| DOI | 10.1109/IGESSC50231.2020.9285088 |
| Citation Key | sami_rapid_2020 |
Rapid, Multi-vehicle and Feed-forward Neural Network based Intrusion Detection System for Controller Area Network Bus