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CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection

Q: How does CEBin's hierarchical inference process contribute to its performance compared to traditional methods

CEBin's hierarchical inference process plays a crucial role in enhancing its performance compared to traditional methods. By combining both embedding-based and comparison-based approaches in a hierarchical manner, CEBin is able to leverage the strengths of each method while mitigating their weaknesses. In the first stage of the inference process, CEBin uses the embedding model to efficiently retrieve a subset of candidate functions that are most similar to the query function from a large pool. This initial step helps narrow down the search space and improve efficiency. Subsequently, in the second stage, CEBin employs a comparison model to perform more detailed and nuanced similarity assessments on this subset of candidates identified by the embedding model. This allows for fine-grained analysis and accurate identification of similar code fragments. Overall, this hierarchical approach ensures that CEBin strikes a balance between accuracy and efficiency by utilizing both models effectively in different stages of the inference process. This design choice enables CEBin to outperform traditional methods that rely solely on either embedding-based or comparison-based techniques.

Q: What potential implications could CEBin have for enhancing cybersecurity measures in software ecosystems

The implications of CEBin for enhancing cybersecurity measures in software ecosystems are significant. By providing an effective solution for detecting similar code (including vulnerable ones) in large-scale software environments, CEBin can greatly improve security practices within organizations. One key implication is improved vulnerability detection capabilities. With its ability to accurately identify similarities between binary code fragments across different compilers, architectures, and optimization levels, CEBin can aid in identifying potential vulnerabilities within software systems more efficiently. Additionally, by offering precise evaluation schemes like constructing large benchmarks of vulnerabilities as demonstrated in the research context provided earlier, CEBin contributes towards creating standardized methodologies for assessing BCSD methods specifically tailored for 1-day vulnerability detection tasks. Furthermore, integrating advanced pre-trained models into CEBin could further enhance its capabilities by leveraging state-of-the-art features learned from vast amounts of data. This integration could lead to even higher accuracy rates and faster processing speeds when detecting similarities among binary code fragments.

Q: How might the integration of advanced pre-trained models further improve the capabilities of CEBin for binary code similarity detection

The integration of advanced pre-trained models has immense potential for further improving the capabilities of CEBin for binary code similarity detection. These models have been trained on extensive datasets using sophisticated algorithms like BERT or other deep learning architectures which excel at feature extraction tasks. By incorporating these pre-trained models into CEBin's framework, it can benefit from their superior feature representation abilities which capture complex relationships within binary code more effectively than traditional handcrafted features or simpler neural network architectures. This integration would likely result in enhanced performance metrics such as increased accuracy rates and improved recall values during similarity detection tasks across diverse software ecosystems with varying compilers and optimizations levels.

Kernekoncepter

CEBin proposes a cost-effective framework that combines embedding-based and comparison-based approaches to enhance accuracy while minimizing overheads in large-scale binary code similarity detection.

Resumé

CEBin introduces a novel approach to binary code similarity detection, addressing challenges in accuracy and efficiency. By fusing embedding-based and comparison-based methods, CEBin significantly improves performance in cross-architecture and cross-compiler scenarios. Experimental results demonstrate its superiority over existing state-of-the-art solutions.

CEBin's innovative design choices, such as the Reusable Embedding Cache Mechanism, contribute to its exceptional performance. The hierarchical inference process ensures efficient and accurate similarity detection in large-scale software ecosystems. Furthermore, CEBin showcases robustness across different optimization levels and architectures, highlighting its versatility and effectiveness in real-world applications.

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Statistik

CEBin-E achieves Recall@1 of 0.709 with an RECM size of 8192 on the most challenging task (O0 and O3) in BinaryCorp dataset.
CEBin outperforms baselines across various optimization pairs at a poolsize of 10,000 on BinaryCorp.
CEBin demonstrates superior performance compared to existing solutions on Cisco and Trex datasets for cross-architecture and cross-compiler tasks.

Citater

Vigtigste indsigter udtrukket fra

CEBin

by Hao Wang,Zey... kl. arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18818.pdf

Dybere Forespørgsler

How does CEBin's hierarchical inference process contribute to its performance compared to traditional methods

CEBin's hierarchical inference process plays a crucial role in enhancing its performance compared to traditional methods. By combining both embedding-based and comparison-based approaches in a hierarchical manner, CEBin is able to leverage the strengths of each method while mitigating their weaknesses.
In the first stage of the inference process, CEBin uses the embedding model to efficiently retrieve a subset of candidate functions that are most similar to the query function from a large pool. This initial step helps narrow down the search space and improve efficiency.
Subsequently, in the second stage, CEBin employs a comparison model to perform more detailed and nuanced similarity assessments on this subset of candidates identified by the embedding model. This allows for fine-grained analysis and accurate identification of similar code fragments.
Overall, this hierarchical approach ensures that CEBin strikes a balance between accuracy and efficiency by utilizing both models effectively in different stages of the inference process. This design choice enables CEBin to outperform traditional methods that rely solely on either embedding-based or comparison-based techniques.

What potential implications could CEBin have for enhancing cybersecurity measures in software ecosystems

The implications of CEBin for enhancing cybersecurity measures in software ecosystems are significant. By providing an effective solution for detecting similar code (including vulnerable ones) in large-scale software environments, CEBin can greatly improve security practices within organizations.
One key implication is improved vulnerability detection capabilities. With its ability to accurately identify similarities between binary code fragments across different compilers, architectures, and optimization levels, CEBin can aid in identifying potential vulnerabilities within software systems more efficiently.
Additionally, by offering precise evaluation schemes like constructing large benchmarks of vulnerabilities as demonstrated in the research context provided earlier, CEBin contributes towards creating standardized methodologies for assessing BCSD methods specifically tailored for 1-day vulnerability detection tasks.
Furthermore, integrating advanced pre-trained models into CEBin could further enhance its capabilities by leveraging state-of-the-art features learned from vast amounts of data. This integration could lead to even higher accuracy rates and faster processing speeds when detecting similarities among binary code fragments.

How might the integration of advanced pre-trained models further improve the capabilities of CEBin for binary code similarity detection

The integration of advanced pre-trained models has immense potential for further improving the capabilities of CEBin for binary code similarity detection. These models have been trained on extensive datasets using sophisticated algorithms like BERT or other deep learning architectures which excel at feature extraction tasks.
By incorporating these pre-trained models into CEBin's framework, it can benefit from their superior feature representation abilities which capture complex relationships within binary code more effectively than traditional handcrafted features or simpler neural network architectures.
This integration would likely result in enhanced performance metrics such as increased accuracy rates and improved recall values during similarity detection tasks across diverse software ecosystems with varying compilers and optimizations levels.