{"total":118,"rows":[{"id":"2310.16340","title":"RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models","created":"2023-10-25","cited_by_count":65,"area":"agents"},{"id":"2108.01955","title":"Log-based Anomaly Detection Without Log Parsing","created":"2021-08-04","cited_by_count":59,"area":null},{"id":"2202.04301","title":"Log-based Anomaly Detection with Deep Learning: How Far Are We?","created":"2022-02-09","cited_by_count":57,"area":null},{"id":"2111.09564","title":"LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model","created":"2021-11-18","cited_by_count":30,"area":null},{"id":"2207.03820","title":"Deep Learning for Anomaly Detection in Log Data: A Survey","created":"2022-07-08","cited_by_count":28,"area":null},{"id":"2309.14482","title":"LogGPT: Log Anomaly Detection via GPT","created":"2023-09-25","cited_by_count":26,"area":null},{"id":"2404.12135","title":"mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture","created":"2024-04-18","cited_by_count":26,"area":"agents"},{"id":"2411.08561","title":"LogLLM: Log-based Anomaly Detection Using Large Language Models","created":"2024-11-13","cited_by_count":22,"area":null},{"id":"2408.07583","title":"Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey","created":"2024-08-14","cited_by_count":21,"area":null},{"id":"2210.11298","title":"Tele-Knowledge Pre-training for Fault Analysis","created":"2022-10-20","cited_by_count":18,"area":null},{"id":"2210.09439","title":"CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model","created":"2022-10-17","cited_by_count":17,"area":null},{"id":"2304.04661","title":"AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges","created":"2023-04-10","cited_by_count":15,"area":null},{"id":"2205.02100","title":"MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series","created":"2022-05-04","cited_by_count":12,"area":null},{"id":"2112.03159","title":"UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks","created":"2021-12-06","cited_by_count":11,"area":null},{"id":"2605.26563","title":"TrajAudit: Automated Failure Diagnosis for Agentic Coding Systems","created":"2026-05-26","cited_by_count":11,"area":"agents"},{"id":"2109.09537","title":"A2Log: Attentive Augmented Log Anomaly Detection","created":"2021-09-20","cited_by_count":10,"area":null},{"id":"2204.12822","title":"A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions","created":"2022-04-27","cited_by_count":9,"area":null},{"id":"2509.10858","title":"Large Language Models for Security Operations Centers: A Comprehensive Survey","created":"2025-09-13","cited_by_count":9,"area":null},{"id":"2208.10833","title":"LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction","created":"2022-08-23","cited_by_count":8,"area":null},{"id":"2307.00527","title":"Graph Neural Networks based Log Anomaly Detection and Explanation","created":"2023-07-02","cited_by_count":8,"area":null},{"id":"2309.02854","title":"A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection Techniques","created":"2023-09-06","cited_by_count":8,"area":null},{"id":"2409.04834","title":"Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study","created":"2024-09-07","cited_by_count":8,"area":null},{"id":"2412.01377","title":"Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge","created":"2024-12-02","cited_by_count":8,"area":null},{"id":"2104.07324","title":"OneLog: Towards End-to-End Training in Software Log Anomaly Detection","created":"2021-04-15","cited_by_count":7,"area":null},{"id":"2407.06085","title":"LLMcap: Large Language Model for Unsupervised PCAP Failure Detection","created":"2024-07-03","cited_by_count":7,"area":null},{"id":"2308.11526","title":"Learning Representations on Logs for AIOps","created":"2023-08-18","cited_by_count":6,"area":null},{"id":"2407.17545","title":"Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning","created":"2024-07-24","cited_by_count":6,"area":null},{"id":"2507.20051","title":"$K^4$: Online Log Anomaly Detection Via Unsupervised Typicality Learning","created":"2025-07-26","cited_by_count":6,"area":null},{"id":"2111.13462","title":"A Taxonomy of Anomalies in Log Data","created":"2021-11-26","cited_by_count":5,"area":null},{"id":"2409.02474","title":"A Comparative Study on Large Language Models for Log Parsing","created":"2024-09-04","cited_by_count":5,"area":null},{"id":"2411.01176","title":"CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research","created":"2024-11-02","cited_by_count":5,"area":null},{"id":"2402.10397","title":"LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs","created":"2024-02-16","cited_by_count":4,"area":null},{"id":"2404.08750","title":"FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and Discrimination","created":"2024-04-12","cited_by_count":4,"area":null},{"id":"2505.00841","title":"From Texts to Shields: Convergence of Large Language Models and Cybersecurity","created":"2025-05-01","cited_by_count":4,"area":null},{"id":"2507.02390","title":"Evaluating Language Models For Threat Detection in IoT Security Logs","created":"2025-07-03","cited_by_count":4,"area":null},{"id":"2508.12412","title":"LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems","created":"2025-08-17","cited_by_count":4,"area":"agents"},{"id":"2208.09440","title":"Feature Selection for Fault Detection and Prediction based on Event Log Analysis","created":"2022-08-19","cited_by_count":3,"area":null},{"id":"2301.10681","title":"PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning","created":"2023-01-25","cited_by_count":3,"area":null},{"id":"2309.05953","title":"GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection","created":"2023-09-12","cited_by_count":3,"area":null},{"id":"2407.18462","title":"MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detection","created":"2024-07-26","cited_by_count":3,"area":null},{"id":"2503.08045","title":"Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2503.14849","title":"LogLLaMA: Transformer-based log anomaly detection with LLaMA","created":"2025-03-19","cited_by_count":3,"area":null},{"id":"2210.04145","title":"Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations","created":"2022-10-09","cited_by_count":2,"area":null},{"id":"2310.13247","title":"Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches","created":"2023-10-20","cited_by_count":2,"area":null},{"id":"2409.01909","title":"LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models","created":"2024-09-03","cited_by_count":2,"area":null},{"id":"2409.03046","title":"Oddballness: universal anomaly detection with language models","created":"2024-09-04","cited_by_count":2,"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":"2505.06701","title":"RuleGenie: SIEM Detection Rule Set Optimization","created":"2025-05-10","cited_by_count":2,"area":null},{"id":"2505.13496","title":"ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model","created":"2025-05-15","cited_by_count":2,"area":null},{"id":"2507.11071","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","created":"2025-07-15","cited_by_count":2,"area":null},{"id":"2509.16625","title":"Self-Supervised Learning of Graph Representations for Network Intrusion Detection","created":"2025-09-20","cited_by_count":2,"area":null},{"id":"2512.01672","title":"ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models","created":"2025-12-01","cited_by_count":2,"area":null},{"id":"2606.10281","title":"Benchmarking and Exploring the Capabilities of LLMs for Attack Investigations","created":"2026-06-09","cited_by_count":2,"area":null},{"id":"2308.10055","title":"Robust Fraud Detection via Supervised Contrastive Learning","created":"2023-08-19","cited_by_count":1,"area":null},{"id":"2309.02206","title":"Language Models for Novelty Detection in System Call Traces","created":"2023-09-05","cited_by_count":1,"area":null},{"id":"2402.10283","title":"Backdoor Attack against One-Class Sequential Anomaly Detection Models","created":"2024-02-15","cited_by_count":1,"area":null},{"id":"2509.05936","title":"ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detection","created":"2025-09-07","cited_by_count":1,"area":null},{"id":"2510.16044","title":"A Novel GPT-Based Framework for Anomaly Detection in System Logs","created":"2025-10-16","cited_by_count":1,"area":null},{"id":"2511.18739","title":"A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2512.06906","title":"MINES: Explainable Anomaly Detection through Web API Invariant Inference","created":"2025-12-07","cited_by_count":1,"area":null},{"id":"2512.08277","title":"FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2603.03378","title":"AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis","created":"2026-03-03","cited_by_count":1,"area":null},{"id":"2604.12218","title":"LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics","created":"2026-04-14","cited_by_count":1,"area":null},{"id":"2605.06455","title":"PrefixGuard: From LLM-Agent Traces to Online Failure-Warning Monitors","created":"2026-05-07","cited_by_count":1,"area":"agents"},{"id":"2606.04799","title":"UModel: An Agent-Ready Observability Data Modeling Method at Scale","created":"2026-06-03","cited_by_count":1,"area":"agents"},{"id":"2209.14727","title":"FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS","created":"2022-09-29","cited_by_count":0,"area":null},{"id":"2301.07846","title":"ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection","created":"2023-01-19","cited_by_count":0,"area":null},{"id":"2304.03064","title":"An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset","created":"2023-04-06","cited_by_count":0,"area":null},{"id":"2305.09673","title":"Vulnerability Detection Using Two-Stage Deep Learning Models","created":"2023-05-08","cited_by_count":0,"area":null},{"id":"2306.09260","title":"IsoEx: an explainable unsupervised approach to process event logs cyber investigation","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2311.05160","title":"RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information","created":"2023-11-09","cited_by_count":0,"area":null},{"id":"2311.05733","title":"LogShield: A Transformer-based APT Detection System Leveraging Self-Attention","created":"2023-11-09","cited_by_count":0,"area":null},{"id":"2312.14748","title":"Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis","created":"2023-12-22","cited_by_count":0,"area":null},{"id":"2401.07655","title":"MLAD: A Unified Model for Multi-system Log Anomaly Detection","created":"2024-01-15","cited_by_count":0,"area":null},{"id":"2403.04293","title":"MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network","created":"2024-03-07","cited_by_count":0,"area":null},{"id":"2403.10327","title":"Unsupervised Threat Hunting using Continuous Bag-of-Terms-and-Time (CBoTT)","created":"2024-03-15","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":"2407.03759","title":"Convolutional vs Large Language Models for Software Log Classification in Edge-Deployable Cellular Network Testing","created":"2024-07-04","cited_by_count":0,"area":null},{"id":"2409.20503","title":"What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach","created":"2024-09-30","cited_by_count":0,"area":null},{"id":"2411.15250","title":"TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters","created":"2024-11-22","cited_by_count":0,"area":null},{"id":"2501.12166","title":"Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection","created":"2025-01-21","cited_by_count":0,"area":null},{"id":"2505.16223","title":"MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2507.01077","title":"Good Enough to Learn: LLM-based Anomaly Detection in ECU Logs without Reliable Labels","created":"2025-07-01","cited_by_count":0,"area":null},{"id":"2508.01844","title":"Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environments","created":"2025-08-03","cited_by_count":0,"area":null},{"id":"2508.05690","title":"Leveraging large language models for SQL behavior-based database intrusion detection","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2508.05695","title":"MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection","created":"2025-08-06","cited_by_count":0,"area":"ssm"},{"id":"2511.14062","title":"LogPurge: Log Data Purification for Anomaly Detection via Rule-Enhanced Filtering","created":"2025-11-18","cited_by_count":0,"area":null},{"id":"2512.19697","title":"Automated Fault Detection in 5G Core Networks Using Large Language Models","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2512.22903","title":"Debugging Tabular Log as Dynamic Graphs","created":"2025-12-28","cited_by_count":0,"area":null},{"id":"2601.14336","title":"Log anomaly detection via Meta Learning and Prototypical Networks for Cross domain generalization","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2602.03678","title":"ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.06777","title":"Next-generation cyberattack detection with large language models: anomaly analysis across heterogeneous logs","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2602.07303","title":"KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2602.07698","title":"On Sequence-to-Sequence Models for Automated Log Parsing","created":"2026-02-07","cited_by_count":0,"area":"ssm"},{"id":"2604.05168","title":"Instruction-Tuned LLMs for Parsing and Mining Unstructured Logs on Leadership HPC Systems","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2604.13024","title":"CLAD: Efficient Log Anomaly Detection Directly on Compressed Representations","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2604.19118","title":"DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs","created":"2026-04-21","cited_by_count":0,"area":null},{"id":"2605.06906","title":"TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond","created":"2026-05-07","cited_by_count":0,"area":"lr"},{"id":"2605.10988","title":"Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2605.18015","title":"LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems","created":"2026-05-18","cited_by_count":0,"area":null}],"next_cursor":"100"}