GNN-based Control-Flow Attestation for Embedded Devices: Overcoming Limitations with RAGE

Centrala begrepp

The author introduces RAGE, a novel CFA approach for embedded devices, addressing limitations of existing schemes by leveraging Graph Neural Networks to detect Code Reuse Attacks efficiently.

Sammanfattning

The paper introduces RAGE, a lightweight CFA approach for embedded devices that overcomes limitations of existing schemes. By utilizing Unsupervised Graph Neural Networks, RAGE can efficiently detect Code Reuse Attacks and achieve high accuracy in detecting real-world and synthetic attacks on embedded software.
Existing Control-Flow Attestation (CFA) schemes face challenges due to impractical assumptions and high computational overhead. The introduction of RAGE aims to address these limitations by providing a novel approach with minimal requirements. By leveraging the correspondence between execution trace, graph, and embeddings, RAGE eliminates the need for a complete Control-Flow Graph (CFG).
RAGE is evaluated on various benchmarks and real-world attacks, showcasing its effectiveness in detecting malicious activities while maintaining low False Positive Rates. The paper also discusses the training process using Variational Graph Autoencoders (VGAEs) and the importance of feature extraction in preparing data for machine learning.
Overall, RAGE presents a promising solution for efficient control-flow attestation in embedded devices by introducing a novel approach that leverages Graph Neural Networks to detect code reuse attacks effectively.

Statistik

"RAGE can detect Code Reuse Attacks (CRA), achieving 98.03% F1-Score for ROP attacks."
"RAGE achieves 97.49% F1-Score for DOP attack detection on OpenSSL."
"RAGE maintains a low False Positive Rate of 3.19%."

Citat

Viktiga insikter från

One for All and All for One

by Marco Chiles... på arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07465.pdf

Djupare frågor

Is there potential for RAGE to be implemented in other security services beyond control-flow attestation

RAGE, with its novel lightweight CFA approach using Unsupervised Graph Neural Networks (GNNs), has the potential to be implemented in various other security services beyond control-flow attestation. The use of GNNs allows for efficient feature extraction and anomaly detection in graph-structured data, making it applicable to a wide range of security applications. For instance, RAGE could be utilized in intrusion detection systems to identify unusual patterns in network traffic or detect anomalies in system logs indicative of cyber attacks. Furthermore, the model's ability to generalize over benign execution paths can be leveraged for malware detection by identifying deviations from normal software behavior based on execution traces.

How does the use of Unsupervised Graph Neural Networks impact the scalability of the solution

The use of Unsupervised Graph Neural Networks impacts the scalability of the solution positively by enabling efficient processing and analysis of large-scale graph data without requiring labeled training data. GNNs are well-suited for handling complex relationships and structures within graphs, allowing RAGE to scale effectively as the size and complexity of the execution traces increase. By leveraging unsupervised learning techniques, RAGE can adapt to new environments and datasets without manual annotation or supervision, making it versatile and scalable across different applications and datasets.

How can the findings from this research be applied to enhance security measures in IoT devices

The findings from this research can significantly enhance security measures in IoT devices by providing a robust method for detecting code reuse attacks like return-oriented programming (ROP) and data-oriented programming (DOP). Implementing RAGE on IoT devices can help protect against sophisticated runtime attacks that exploit vulnerabilities in embedded software. By utilizing unsupervised machine learning models like VGAEs, IoT devices can continuously monitor their software integrity during runtime without relying on extensive resources or specialized hardware. This proactive approach enhances overall cybersecurity posture for IoT ecosystems by detecting malicious activities at an early stage before they cause significant damage or compromise sensitive information stored on these devices.