[CB24]Attention Is All You Need for Semantics Detection:A Novel Transformer on Neural…

Malware analysis challenge

Trend Micro researchers at Code Blue explain that obfuscated and packed Windows malware overwhelms analysts. Human experts infer unknown APIs from argument counts, data types, and cross-reference chains, but that reasoning does not scale.

Prior work and gaps

Their earlier Master at Black Hat USA 2022 used taint analysis and YARA-like rules, yet rule maintenance remained impractical. They propose Kur-IDA, a practical neuro-symbolic Transformer with cached inference and a symbolic engine to recover API argument values from stripped binaries without execution.

Semantic attention

Unlike prior static models with lower-precision semantics, they apply precision-wise semantic encoding in scaled dot-product attention to capture both numeric argument patterns and API-specific meaning. The approach tokenizes integers by inferred data types rather than embedding the entire 64-bit space, then feeds predicted API sequences into analyst tooling for real-world Blue Team workflows.