Analyzing Cryptographic Functions in Stripped Binaries with FoC Framework

Kernekoncepter

FoC framework efficiently analyzes cryptographic functions in stripped binaries.

Resumé

The paper introduces the FoC framework for analyzing cryptographic functions in stripped binaries. It proposes a novel approach using FoC-BinLLM and FoC-Sim models to summarize semantics and detect similarities in binary code. The framework outperforms existing methods in summarizing and identifying cryptographic functions, showcasing practical abilities in virus analysis and vulnerability detection.

Structure:

1. Introduction to Cryptographic Function Analysis
   • Importance of analyzing cryptographic functions in stripped binaries.
2. FoC Framework Overview
   • Introduction to FoC-BinLLM and FoC-Sim models.
3. Dataset Construction
   • Collection of cryptographic binary dataset for training and evaluation.
4. Binary Large Language Model
   • Training setup and performance evaluation for summarizing binary code.
5. Binary Code Similarity Model
   • Training setup and performance evaluation for detecting similarities in binary code.
6. Practical Ability
   • Evaluation of FoC framework's practical abilities in real-world scenarios.

Statistik

Evaluation results demonstrate that FoC-BinLLM outperforms ChatGPT by 14.61% on the ROUGE-L score.
FoC-Sim outperforms the previous best methods with a 52% higher Recall@1.

Citater

"In this paper, we propose a novel framework called FoC to Figure out the Cryptographic functions in stripped binaries."
"Our contributions can be summarized as follows: We construct a cryptographic binary dataset cross-compiled from popular open-source repositories written in C language."

Vigtigste indsigter udtrukket fra

FoC

by Guoqiang Che... kl. arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18403.pdf

Dybere Forespørgsler

How can the FoC framework be applied to other domains beyond cryptography

The FoC framework can be applied to other domains beyond cryptography by adapting the model to analyze different types of functions in stripped binaries. For example, the binary large language model (FoC-BinLLM) can be trained on datasets from various software domains such as networking protocols, file systems, or system utilities. By adjusting the semantic labels and features used in the model, FoC can be tailored to identify and summarize functions specific to these domains. This flexibility allows the framework to be utilized in a wide range of applications where understanding the behavior of binary functions is crucial.

What counterarguments exist against the effectiveness of the FoC framework in analyzing stripped binaries

Counterarguments against the effectiveness of the FoC framework in analyzing stripped binaries may include concerns about the generalizability of the model across different architectures and compilation environments. Since the model relies on semantic labels and features extracted from the dataset, variations in coding styles, optimizations, or platform-specific implementations could impact the accuracy of the analysis. Additionally, the complexity of cryptographic functions and the absence of symbolic information in stripped binaries may pose challenges for the model in accurately summarizing and identifying these functions. Furthermore, the reliance on pre-trained models and frozen-decoder training strategies may limit the adaptability of the framework to new and evolving threats in the cybersecurity landscape.

How can the insights from the FoC framework be utilized in enhancing cybersecurity measures

The insights from the FoC framework can be utilized to enhance cybersecurity measures by improving the detection and analysis of malicious code in software systems. By leveraging the semantic understanding of binary functions provided by FoC-BinLLM, cybersecurity professionals can identify potential vulnerabilities, backdoors, or malicious behaviors in software binaries. The binary code similarity model (FoC-Sim) can aid in detecting similarities between known malicious functions and newly discovered code, enabling faster threat response and mitigation. Additionally, the cryptographic features identified by FoC can be used to strengthen encryption protocols, identify weak cryptographic implementations, and enhance overall data security measures. By integrating the insights from FoC into cybersecurity tools and practices, organizations can bolster their defenses against cyber threats and ensure the integrity of their software systems.