Hardware-assisted Detection of Malware in Automotive-Based Systems
Yugpratap Singh1,3,a, Abraham Peedikayil Kuruvila1,3,b and Kanad Basu2,3,c
1Student Member, IEEE
2Senior Member, IEEE
3The University of Texas at Dallas
ayps170000@utdallas.edu
bapk190000@utdallas.edu
ckxb190012@utdallas.edu
ABSTRACT
In the age of Internet-of-Things (IoT), automobiles have become heavily integrated and reliant on computerized components for system functionality. Modern vehicles have many Electronic Control Units (ECUs) that control ignition timing, suspension control, and transmission shifting. The Engine Control Module (ECM) is generally recognized as one of the most essential components owing to its functionality of regulating air and fuel input to the engine. Consequently, automotive security is an emerging problem that will only escalate as vehicles integrate more computerized components in conjunction with wireless system connectivity. Attackers that successfully gain access to important vehicular components and compromise existing functionality can induce a plethora of malevolent activities. With the evolution and exponential proliferation of Malware, identifying malicious entities is critical for maintaining proper system performance. Traditional anti-virus software is inadequate against complex Malware, which has engendered a push towards Hardware-assisted Malware Detection (HMDs) using Hardware Performance Counters (HPCs). HPCs are special purpose registers that track low-level micro-architectural events. In this paper, we propose using Machine Learning models trained on HPC data to identify malicious entities in the ECM. Our experimental results determine that the proposed MLbased models can successfully identify malicious actions in an automotive system with a classification accuracy of up to 96.7%.
Keywords: Automotive Security, Hardware Performance Counters, Machine Learning, Cyber-physical systems.
