{"total":20,"rows":[{"id":"2308.05034","title":"Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance","created":"2023-08-09","cited_by_count":31,"area":null},{"id":"2111.04333","title":"threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning","created":"2021-11-08","cited_by_count":22,"area":null},{"id":"2310.09831","title":"MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning","created":"2023-10-15","cited_by_count":20,"area":null},{"id":"2104.08618","title":"EXTRACTOR: Extracting Attack Behavior from Threat Reports","created":"2021-04-17","cited_by_count":19,"area":null},{"id":"2105.10053","title":"A Rule Mining-Based Advanced Persistent Threats Detection System","created":"2021-05-20","cited_by_count":8,"area":null},{"id":"2402.15147","title":"TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning","created":"2024-02-23","cited_by_count":6,"area":null},{"id":"2510.15188","title":"OCR-APT: Reconstructing APT Stories from Audit Logs using Subgraph Anomaly Detection and LLMs","created":"2025-10-16","cited_by_count":6,"area":null},{"id":"2112.11032","title":"ANUBIS: A Provenance Graph-Based Framework for Advanced Persistent Threat Detection","created":"2021-12-21","cited_by_count":3,"area":null},{"id":"2503.03108","title":"OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Prompting","created":"2025-03-05","cited_by_count":3,"area":null},{"id":"2504.07839","title":"Deep Learning-based Intrusion Detection Systems: A Survey","created":"2025-04-10","cited_by_count":2,"area":null},{"id":"2104.10319","title":"Evidential Cyber Threat Hunting","created":"2021-04-21","cited_by_count":1,"area":null},{"id":"2211.13345","title":"Principled Data-Driven Decision Support for Cyber-Forensic Investigations","created":"2022-11-23","cited_by_count":1,"area":null},{"id":"2304.02838","title":"TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph","created":"2023-04-06","cited_by_count":1,"area":null},{"id":"2601.22983","title":"PIDSMaker: Building and Evaluating Provenance-based Intrusion Detection Systems","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2112.08986","title":"A Heterogeneous Graph Learning Model for Cyber-Attack Detection","created":"2021-12-16","cited_by_count":0,"area":null},{"id":"2406.05362","title":"RAPID: Robust APT Detection and Investigation Using Context-Aware Deep Learning","created":"2024-06-08","cited_by_count":0,"area":null},{"id":"2509.26562","title":"DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2602.02929","title":"RPG-AE: Neuro-Symbolic Graph Autoencoders with Rare Pattern Mining for Provenance-Based Anomaly Detection","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2608.01454","title":"How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection","created":"2026-08-02","cited_by_count":0,"area":null},{"id":"2609.16462","title":"Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection","created":"2026-09-15","cited_by_count":0,"area":null}],"next_cursor":null}