Cerisier is the first mechanized program logic for modular reasoning about trusted, untrusted, and attested code in capability machines, with a universal contract for untrusted code and demonstrations on secure computation and mutual attestation.
Mixed citations
SoK: Pragmatic Assess- ment of Machine Learning for Network Intrusion Detection, in: Pro- ceedings of the IEEE European Symposium on Security and Privacy, IEEE, Delft, Netherlands
Mixed citation behavior. Most common role is background (60%).
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60% of classified citations
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representative citing papers
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
Roblox's automated chat moderation fails to catch numerous unsafe messages involving grooming, sexualization of minors, bullying, violence, self-harm, and sensitive information sharing, with users evading detection through various techniques.
Generative AI boosts attackers' ability to create harmful content at scale while also enabling defenders to detect threats, support users, and improve moderation processes.
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.
citing papers explorer
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Cerisier: A Program Logic for Attestation in a Capability Machine
Cerisier is the first mechanized program logic for modular reasoning about trusted, untrusted, and attested code in capability machines, with a universal contract for untrusted code and demonstrations on secure computation and mutual attestation.
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What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
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An Evaluation of Chat Safety Moderations in Roblox
Roblox's automated chat moderation fails to catch numerous unsafe messages involving grooming, sexualization of minors, bullying, violence, self-harm, and sensitive information sharing, with users evading detection through various techniques.
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How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape
Generative AI boosts attackers' ability to create harmful content at scale while also enabling defenders to detect threats, support users, and improve moderation processes.
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AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.