MimeLens: Position-Agnostic Content-Type Detection for Binary Fragments

1. 1Build a dataset of binary fragments with corresponding content types
2. 2Train a BERT-style encoder using the dataset
3. 3Configure the encoder to handle position-agnostic inputs
4. 4Test the encoder on various binary fragments
5. 5Apply the MimeLens approach to real-world workflows like malware triage and forensic carving

Developers and researchers working on file-type classification, malware detection, and forensic analysis can benefit from MimeLens, as it enhances the accuracy of content-type detection in binary fragments

💡 MimeLens uses small BERT-style encoders to detect content types in binary fragments, even when file offsets are unknown

🔍 Introducing MimeLens: accurate content-type detection for binary fragments without relying on file offsets #filetypeclassification #malwaredetection