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These challenges are amplified in cross-operating-system (cross-OS) settings, where a detector trained on one source platform must be deployed on an unlabeled target platform without access to target-domain labels. We study this source-only cross-OS APT detection problem using system-level provenance traces and propose a transport-based framework for ranking anomalous target processes under zero target supervision. The framework abstracts process behavior into structured natural-language descriptions, embeds them using pretrained language models, and constructs a source-normal reference for target scoring. It combines three evidence channels: semantic deviation from source-normal prototypes, structural deviation captured","title":"A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport","url":"https://arxiv.org/abs/2606.10216","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.10216v1 Announce Type: cross \nAbstract: Advanced Persistent Threats (APTs) are stealthy, multi-stage cyberattacks whose detection is difficult due to scarce labeled traces, severe class imbalance, and the challenge of generating realistic malicious behavior. These challenges are amplified in cross-operating-system (cross-OS) settings, where a detector trained on one source platform must be deployed on an unlabeled target platform without access to target-domain labels. We study this source-only cross-OS APT detection problem using system-level provenance traces and propose a transport-based framework for ranking anomalous target processes under zero target supervision. The framework abstracts process behavior into structured natural-language descriptions, embeds them using pretrained language models, and constructs a source-normal reference for target scoring. 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