Multimodal Instruction Disassembly with Covariate Shift Adaptation and Real-time Implementation

TL;DR

This paper presents a real-time, multimodal side-channel disassembly system using a new platform and covariate shift adaptation, significantly improving accuracy and practicality for security applications.

Contribution

Introduction of RASCv3 platform, a novel feature selection method, and covariate shift techniques for real-time, adaptive instruction disassembly from power and EM signals.

Findings

Achieved high recognition rates on six benchmarks

Demonstrated effectiveness of multimodal data and covariate shift adjustment

Compared various classifiers for optimal performance

Abstract

Side-channel based instruction disassembly has been proposed as a low-cost and non-invasive approach for security applications such as IP infringement detection, code flow analysis, malware detection, and reconstructing unknown code from obsolete systems. However, existing approaches to side-channel based disassembly rely on setups to collect and process side-channel traces that make them impractical for real-time applications. In addition, they rely on fixed classifiers that cannot adapt to statistical deviations in side-channels caused by different operating environments. In this article, we advance the state of the art in side-channel based disassembly in multiple ways. First, we introduce a new miniature platform, RASCv3, that can simultaneously collect power and EM measurements from a target device and subsequently process them for instruction disassembly in real time. Second, we devise a new approach to combine and select features from power and EM traces using information theory that improves classification accuracy and avoids the curse of dimensionality. Third, we explore covariate shift adjustment techniques that further improve accuracy over time and in response to statistical changes. The proposed methodology is demonstrated on six benchmarks, and the recognition rates of offline and real-time instruction disassemblers are compared for single- and multi-modal cases with a variety of classifiers and over time. Since the proposed approach is only applied to an 8-bit Arduino UNO, we also discuss challenges of extending to more complex targets.