A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport
arXiv:2606.10216v1 Announce Type: new Abstract: 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-
The increasing sophistication of cyber threats like APTs and the challenges in traditional detection methods, especially in complex cross-OS environments, necessitate advanced AI/ML solutions.
This research addresses a critical cybersecurity vulnerability by proposing a more robust and adaptable method for detecting advanced persistent threats across diverse operating systems, reducing the burden of target-domain labeling.
The development of 'source-only' transfer learning for APT detection could significantly improve the resilience of critical infrastructure against sophisticated cyberattacks, simplifying deployment and reducing training data requirements.
- · Cybersecurity companies
- · Organizations with diverse IT infrastructure
- · National security agencies
- · AI/ML security researchers
- · State-sponsored cyber attackers
- · Cyber criminals
- · Legacy cybersecurity vendors
Enhanced cybersecurity posture for organizations operating across multiple operating systems due to improved APT detection.
Reduced operational costs and a faster response time for new threat detection, as less target-specific data is needed for model deployment.
A potential arms race in cyber defense, where advancements in AI-driven detection are met with increasingly sophisticated adversarial AI techniques.
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Read at arXiv cs.LG