A new queryable binary dataset combining cross-build diversity, temporal history, and CVE labels with linked metadata for vulnerability research.
Neural Network- based Graph Embedding for Cross-Platform Binary Code Similarity Detection
10 Pith papers cite this work. Polarity classification is still indexing.
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Large-scale study on 60k firmware shows vulnerable function versions, search space, function sizes and compilation toolchains affect BCSD performance; build-aware queries raise MRR from 0.818 to 0.981 and TPL-aware two-stage search improves it by 18.5%.
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.
Analytical model of Bitcoin mining rewards under tie-breaking rules finds no incentive for miners to propagate others' blocks, with first-seen rule maximizing propagation incentives but minimizing fairness.
LogCopilot is an LLM framework that builds a hierarchical knowledge base from logs and generates/executes LogQL queries from natural language instructions, reporting 76.8% average accuracy across four datasets.
A two-layer GCN on 21,438 request-level invocation graphs from a Docker-based microservice benchmark reaches 96.2% accuracy under random split but is outperformed by non-graph baselines under stricter trial-level splits.
A self-supervised GNN model on cloud logs flags suspicious events with far fewer alerts than rule-based baselines but cannot estimate missed threats.
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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Understanding Binary Code Similarity for Real-World Vulnerability Detection: A Large-Scale Empirical Study
Large-scale study on 60k firmware shows vulnerable function versions, search space, function sizes and compilation toolchains affect BCSD performance; build-aware queries raise MRR from 0.818 to 0.981 and TPL-aware two-stage search improves it by 18.5%.
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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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On the Incentive Compatibility of Block Propagation in Bitcoin
Analytical model of Bitcoin mining rewards under tie-breaking rules finds no incentive for miners to propagate others' blocks, with first-seen rule maximizing propagation incentives but minimizing fairness.
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LogCopilot: Automating Log Aggregation Analysis through Large Language Models
LogCopilot is an LLM framework that builds a hierarchical knowledge base from logs and generates/executes LogQL queries from natural language instructions, reporting 76.8% average accuracy across four datasets.
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Can Graph-Based Microservice Performance Detection Be Used for Microservice Intrusion Detection?
A two-layer GCN on 21,438 request-level invocation graphs from a Docker-based microservice benchmark reaches 96.2% accuracy under random split but is outperformed by non-graph baselines under stricter trial-level splits.
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Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks
A self-supervised GNN model on cloud logs flags suspicious events with far fewer alerts than rule-based baselines but cannot estimate missed threats.
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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.