A new queryable binary dataset combining cross-build diversity, temporal history, and CVE labels with linked metadata for vulnerability research.
Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , pages =
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Krone-viz provides an interactive interface for hierarchical log decomposition, modular anomaly detection, and human-in-the-loop LLM explanation on system logs.
ERIS partitions client updates into shards aggregated across multiple client-side nodes to reduce communication bottlenecks, limit information exposure, and preserve FedAvg-level utility while improving resistance to inference attacks.
PRAXIS combines LLM-driven structured traversal of service dependency graphs and hammock-block program dependence graphs to improve root-cause analysis accuracy by up to 6.3x while cutting token consumption by 5.3x on 30 real-world cloud incidents.
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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ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage
A new queryable binary dataset combining cross-build diversity, temporal history, and CVE labels with linked metadata for vulnerability research.
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Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation
Krone-viz provides an interactive interface for hierarchical log decomposition, modular anomaly detection, and human-in-the-loop LLM explanation on system logs.
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ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregation
ERIS partitions client updates into shards aggregated across multiple client-side nodes to reduce communication bottlenecks, limit information exposure, and preserve FedAvg-level utility while improving resistance to inference attacks.
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PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis
PRAXIS combines LLM-driven structured traversal of service dependency graphs and hammock-block program dependence graphs to improve root-cause analysis accuracy by up to 6.3x while cutting token consumption by 5.3x on 30 real-world cloud incidents.
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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.