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KAIROS: Practical Intrusion Detection and Investigation using Whole-system Provenance
Provenance graphs are structured audit logs that describe the history of a system’s execution. Recent studies have explored a …
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KAIROS: Practical Intrusion Detection and Investigation using Whole-system Provenance
Turbo: Effective Caching in Differentially-Private Databases
The Adam optimizer is a popular choice in contemporary deep learning due to its strong empirical performance. However we observe that …
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Turbo: Effective Caching in Differentially-Private Databases
Turbo: Effective Caching in Differentially-Private Databases
Differentially-private (DP) databases allow for privacy-preserving analytics over sensitive datasets or data streams. In these systems, …
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Turbo: Effective Caching in Differentially-Private Databases
Unleashing Unprivileged eBPF Potential with Dynamic Sandboxing
For safety reasons, unprivileged users today have only limited ways to customize the kernel through the extended Berkeley Packet Filter …
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Unleashing Unprivileged eBPF Potential with Dynamic Sandboxing
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
We develop a new, principled algorithm for estimating the contribution of training data points to the behavior of a deep learning …
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Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation
We observe that the traditional use of DP with the Adam optimizer introduces a bias in the second moment estimation, due to the …
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DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation
Pacer: Comprehensive Network Side-Channel Mitigation in the Cloud
Network side channels (NSCs) leak secrets through packet timing and packet sizes. They are of particular concern in public IaaS Clouds, …
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Pacer: Comprehensive Network Side-Channel Mitigation in the Cloud
Secure Namespaced Kernel Audit for Containers
Despite the wide usage of container-based cloud computing, container auditing for security analysis relies mostly on built-in host …
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Secure Namespaced Kernel Audit for Containers
SIGL: Securing Software Installations Through Deep Graph Learning
Many users implicitly assume that software can only be exploited after it is installed. However, recent supply-chain attacks …
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SIGL: Securing Software Installations Through Deep Graph Learning
UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats
Advanced Persistent Threats (APTs) are difficult to detect due to their low-and-slow attack patterns and frequent use of zero-day …
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UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats