Cosine-similarity-based hard negative selection during contrastive fine-tuning makes LLM embeddings separate malware families more cleanly, improving few-shot multimodal malware classification accuracy by 11 to 21 percentage points on two datasets.
unb.ca/cic/datasets/andmal2020.html, 2020
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings
Cosine-similarity-based hard negative selection during contrastive fine-tuning makes LLM embeddings separate malware families more cleanly, improving few-shot multimodal malware classification accuracy by 11 to 21 percentage points on two datasets.