• Corpus ID: 5876296

DETECTING PACKED EXECUTABLES BASED ON RAW BINARY DATA

@inproceedings{Nataraja2010DETECTINGPE,
  title={DETECTING PACKED EXECUTABLES BASED ON RAW BINARY DATA},
  author={Lakshmanan Nataraja and Gr{\'e}goire Jacobb and B. S. Manjunatha},
  year={2010},
  url={https://api.semanticscholar.org/CorpusID:5876296}
}
A new technique for fast identification of packed executables by analyzing only the raw binary data is proposed and is able to correctly identifypacked executables with a high detection rate in the range of 95%-98% for a variety of packers and crypters.
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11 References

Classification of packed executables for accurate computer virus detection

PE-Probe: Leveraging Packer Detection and Structural Information to Detect Malicious Portable Executables

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