Paper Info
Title
Self-Secured Control With Anomaly Detection And Recovery In Automotive Cyber-Physical Systems
Abstract
Cyber-Physical Systems (CPS) are growing with added complexity and functionality. Multidisciplinary interactions with physical systems are the major keys to CPS. However, sensors, actuators, controllers, and wireless communications are prone to attacks that compromise the system. Machine learning models have been utilized in controllers of automotive to learn, estimate, and provide the required intelligence in the control process. However, their estimation is also vulnerable to the attacks from physical or cyber domains. They have shown unreliable predictions against unknown biases resulted from the modeling. In this paper, we propose a novel control design using conditional generative adversarial networks that will enable a self-secured controller to capture the normal behavior of the control loop and the physical system, detect the anomaly, and recover from them. We experimented our novel control design on a self-secured BMS by driving a Nissan Leaf S on standard driving cycles while under various attacks. The performance of the design has been compared to the state-of-the-art; the self-secured BMS could detect the attacks with 83% accuracy and the recovery estimation error of 21% on average, which have improved by 28% and 8%, respectively.
Year
DOI
Venue
2019
10.23919/DATE.2019.8714833
2019 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)
Keywords
Field
DocType
CPS, Security, Electric Vehicle, Battery, Machine Learning, Battery Management System
Anomaly detection,Computer science,Real-time computing,Cyber-physical system,Automotive industry,Embedded system
Conference
ISSN
Citations 
PageRank 
1530-1591
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Korosh Vatanparvar113416.20
Mohammad Abdullah Al Faruque262765.35