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CATI: Context-Assisted Type Inference from Stripped Binaries | IEEE Conference Publication | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. This paper presents an efficient approach for inferring types from stripped binaries, called CATI.
2. CATI leverages neighboring instructions to enrich the features used to infer variable types.
3. Experiments show that CATI can infer variable type with 71.2% accuracy on unseen binaries and takes approximately 6 seconds to process a typical binary.
Article analysis:
The article is generally reliable and trustworthy, as it provides evidence for its claims in the form of experiments and results. The authors have also provided references to other works in the field, which adds credibility to their work. Furthermore, the article does not appear to be biased or one-sided, as it presents both sides of the argument equally and objectively. However, there are some points of consideration that are missing from the article such as potential risks associated with using CATI or possible limitations of the system that could be explored further. Additionally, there is no mention of any counterarguments or alternative approaches that could be used instead of CATI for type inference from stripped binaries. Finally, there is no promotional content in the article which makes it more credible and trustworthy.