Embedded Device Behavior Detection via Side Channel Analysis
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Solution Overview
Problem
Embedded systems are vulnerable to unauthorized modifications and cyber attacks due to their increased connectivity and critical infrastructure roles, necessitating effective detection methods for ensuring their secure operation.
Innovation Solution
Injecting code or inputs into embedded systems to induce identifiable baseline behaviors in side channel emissions, measuring these emissions, extracting features, and analyzing them to determine the system's behavior and detect unauthorized modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If embedded systems are connected to the Internet and used in critical infrastructure, then their functionality and connectivity are improved, but their vulnerability to cyber attacks increases
Solution Approach 1:
The system performs preliminary actions by establishing a baseline of expected device behavior through side-channel analysis before attacks occur. The monitoring system continuously measures physical emissions (power consumption, electromagnetic radiation, acoustic signals) and compares them against the established baseline to detect deviations indicating cyber attacks, enabling early detection and response.
Solution Approach 2:
The patent introduces side-channel emissions as an intermediary between the embedded device's internal operations and external observation. By measuring physical emissions that leak information about device behavior, the system creates an indirect monitoring channel that reveals unauthorized modifications without directly accessing the device's code or memory, thus detecting attacks while maintaining system integrity.
2Measurement precision
If code injection techniques are used to induce baseline behavior, then the ability to detect unauthorized modifications is improved, but the complexity of the monitoring system increases
Solution Approach 1:
The monitoring system performs self-service by automatically establishing its own baseline of expected device behavior through initial side-channel measurements. Once the baseline is established, the system autonomously continues to measure and compare current emissions against this baseline without requiring external intervention or complex configuration, simplifying operation while maintaining high detection precision.
Solution Approach 2:
The system changes the measurement parameters from direct code analysis to physical side-channel emissions (power consumption, electromagnetic radiation, acoustic signals). This parameter transformation enables detection of unauthorized modifications through physical manifestations of behavioral changes, achieving high measurement precision while avoiding the complexity of invasive code monitoring techniques.
3Reliability
If side channel emissions are measured and analyzed to detect behavior changes, then the detection of unauthorized modifications is improved, but the computational resources required increase
Solution Approach 1:
The system extracts only the most relevant features from the complex side-channel emission data by comparing current measurements against the established baseline. Instead of analyzing all possible parameters, the system identifies and focuses on deviations that indicate unauthorized modifications, extracting meaningful security information while filtering out redundant computational processing requirements.
Solution Approach 2:
The monitoring system implements feedback by continuously comparing current side-channel emissions against the baseline and adjusting its monitoring focus based on detected deviations. When anomalies are detected, the system intensifies analysis; when behavior is normal, it maintains lightweight monitoring, optimizing computational resource consumption while ensuring reliable security monitoring through continuous feedback loops.
Data Source
AI summary
The goal of detecting modifications, such as unauthorized modifications for example, of the code and/or behavior of an embedded device (e.g., unexpected/unauthorized remote reprogramming, re-flashing), changes to code at run-time (e.g., code injection, software parameter changes due to run-time reconfiguration commands), execution of unauthorized code, activation of hardware Trojans, and other attacks on the hardware and/or software of embedded devices (or more generally, for determining an aspect of behavior of an embedded device and/or an embedded system) is solved by (1) injecting at least one of (A) code and/or (B) inputs into the embedded system to cause the embedded system, when functioning as desired, to exhibit an identifiable baseline behavior determined from a sequence of patterns (also referred to as “fiduciary markers”) in observable side channel emissions of the embedded system; (2) measuring side channel emissions generated by the embedded system when the at least one of (A) code and/or (B) inputs is injected; (3) extracting features from the measured side channel emissions; and (4) determining the aspect of the behavior of the embedded system by analyzing the extracted features with respect to features of the baseline behavior.