bliutech/nlp-pdf-malware-detection

Performs static analysis by converting PDFs to byte-string sequences with variable n-gram tokenization and one-hot encoding, then feeds them into a fine-tuned transformer model to achieve 96.67% classification accuracy. The approach targets JavaScript-based PDF exploits without requiring dynamic execution, leveraging transformer attention mechanisms for parallel processing of large malware datasets. Includes full preprocessing pipeline (split, CSV generation, training, validation) and integrates with the CIC-Evasive-PDFMal2022 benchmark dataset.

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