Princeton University is a prestigious Ivy League research institution renowned for its pioneering work in physics, computer science, and public policy. It cultivates fundamental discovery with societal and global impact. NECLA researchers worked with Princeton University to develop enhanced negative sample generation techniques for language-and-vision models. We collaborate to create more informative contrastive learning signals, leading to better alignment between visual inputs and language representations in multimodal AI systems.

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Towards a Timely Causality Analysis for Enterprise Security

The increasingly sophisticated Advanced Persistent Threat (APT) attacks have become a serious challenge for enterprise IT security. Attack causality analysis, which tracks multi-hop causal relationships between files and processes to diagnose attack provenances and consequences, is the first step towards understanding APT attacks and taking appropriate responses. Since attack causality analysis is a time-critical mission, it is essential to design causality tracking systems that extract useful attack information in a timely manner. However, prior work is limited in serving this need. Existing approaches have largely focused on pruning causal dependencies totally irrelevant to the attack, but fail to differentiate and prioritize abnormal events from numerous relevant, yet benign and complicated system operations, resulting in long investigation time and slow responses.