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LogSD: Detecting Anomalies from System Logs through Self-Supervised Learning and Frequency-Based Masking

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Using black-box performance models to detect performance regressions under varying workloads: an empirical study

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Comparing ϕ and the F-measure as performance metrics for software-related classifications

inferredcites →https://doi.org/10.1007/s10664-022-10214-6openalex-snapshot

An empirical study of the impact of log parsers on the performance of log-based anomaly detection

inferredcites →https://doi.org/10.1016/j.icte.2020.06.003openalex-snapshot

Unsupervised log message anomaly detection

inferredcites →https://doi.org/10.1016/j.infsof.2021.106664openalex-snapshot

The impact of using biased performance metrics on software defect prediction research

inferredcites →https://doi.org/10.1016/j.mlwa.2023.100470openalex-snapshot

Deep learning for anomaly detection in log data: A survey

inferredcites →https://doi.org/10.1093/bioinformatics/16.5.412openalex-snapshot

Assessing the accuracy of prediction algorithms for classification: an overview

inferredcites →https://doi.org/10.1109/ase51524.2021.9678773openalex-snapshot

Log-based Anomaly Detection Without Log Parsing

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01553openalex-snapshot

Masked Autoencoders Are Scalable Vision Learners

inferredcites →https://doi.org/10.1109/icac.2004.1301345openalex-snapshot

Failure diagnosis using decision trees

inferredcites →https://doi.org/10.1109/icccn49398.2020openalex-snapshot

2020 29th International Conference on Computer Communications and Networks (ICCCN)

inferredcites →https://doi.org/10.1109/icdm.2007.46openalex-snapshot

Failure Prediction in IBM BlueGene/L Event Logs

inferredcites →https://doi.org/10.1109/icdm51629.2021openalex-snapshot

2021 IEEE International Conference on Data Mining (ICDM)

inferredcites →https://doi.org/10.1109/icse-seip.2019.00021openalex-snapshot

Tools and Benchmarks for Automated Log Parsing

inferredcites →https://doi.org/10.1109/icse43902.2021openalex-snapshot

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

inferredcites →https://doi.org/10.1109/icws.2017.13openalex-snapshot

Drain: An Online Log Parsing Approach with Fixed Depth Tree

inferredcites →https://doi.org/10.1109/srds51746.2020openalex-snapshot

2020 International Symposium on Reliable Distributed Systems (SRDS)

inferredcites →https://doi.org/10.1109/tkde.2005.50openalex-snapshot

Using AUC and accuracy in evaluating learning algorithms

inferredcites →https://doi.org/10.1109/tmm.2022.3175611openalex-snapshot

Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

inferredcites →https://doi.org/10.1145/1401890.1401920openalex-snapshot

Learning classifiers from only positive and unlabeled data

inferredcites →https://doi.org/10.1145/1557019.1557154openalex-snapshot

Clustering event logs using iterative partitioning

inferredcites →https://doi.org/10.1145/1629575.1629587openalex-snapshot

Detecting large-scale system problems by mining console logs

inferredcites →https://doi.org/10.1145/1755913.1755926openalex-snapshot

Fingerprinting the datacenter

inferredcites →https://doi.org/10.1145/2889160.2889232openalex-snapshot

Log clustering based problem identification for online service systems

inferredcites →https://doi.org/10.1145/3133956.3134015openalex-snapshot

DeepLog

inferredcites →https://doi.org/10.1145/3338906.3338931openalex-snapshot

Robust log-based anomaly detection on unstable log data

inferredcites →https://doi.org/10.1145/3488560openalex-snapshot

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

inferredcites →https://doi.org/10.1145/3534678openalex-snapshot

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

inferredcites →https://doi.org/10.1145/3539597.3570404openalex-snapshot

S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking

inferredcites →https://doi.org/10.1145/3540250.3549165openalex-snapshot

The best of both worlds: integrating semantic features with expert features for defect prediction and localization

inferredcites →https://doi.org/10.24963/ijcai.2019/658openalex-snapshot

LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs

inferredcites →https://doi.org/10.3115/v1/d14-1162openalex-snapshot

Glove: Global Vectors for Word Representation

inferredcites →https://doi.org/10.3390/e23020201openalex-snapshot

Unsupervised Anomaly Detection with Distillated Teacher-Student Network Ensemble

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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MobileLogZip: Lightweight LLM-Driven Tiered Log Compression for Mobile Computing

inferred← citeshttps://doi.org/10.1007/978-981-96-9881-3_15openalex-snapshot

DebiasParser: Debiasing LLM-Based Log Parsing via Front-Door Adjustment

inferred← citeshttps://doi.org/10.1007/s10207-026-01294-2openalex-snapshot

Reproducibility in event-log research: a parametrised generator and benchmark for event-based signatures

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.129754openalex-snapshot

Federated transfer learning for anomaly detection in HPC systems: First real-world validation on a tier-0 supercomputer

inferred← citeshttps://doi.org/10.1016/j.future.2025.108090openalex-snapshot

Collaborative anomaly detection in log data: Comparative analysis and evaluation framework

inferred← citeshttps://doi.org/10.1016/j.jss.2025.112431openalex-snapshot

MidLog: An automated log anomaly detection method based on multi-head GRU

inferred← citeshttps://doi.org/10.1063/5.0342061openalex-snapshot

Enhancing anomaly detection in virtual machine logs with LADSVM: A deep learning approach for intelligent operations

inferred← citeshttps://doi.org/10.1108/ijmpb-10-2025-0414openalex-snapshot

Implementing agentic AI in project management: an evidence-based framework and maturity model from systematic analysis

inferred← citeshttps://doi.org/10.1109/apan70967.2026.11709764openalex-snapshot

CTGuard: A Calibrated Cross-System Log Anomaly Detection System for Resilient Cloud and HPC Operations

inferred← citeshttps://doi.org/10.1109/ase63991.2025.00035openalex-snapshot

LogMoE: Lightweight Expert Mixture for Cross-System Log Anomaly Detection

inferred← citeshttps://doi.org/10.1109/ase63991.2025.00161openalex-snapshot

Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning

inferred← citeshttps://doi.org/10.1109/ccsb71176.2026.11716499openalex-snapshot

Research on Fault Prediction Method of Enterprise Data Pipeline Based on ETL Process Logs

inferred← citeshttps://doi.org/10.1109/eiecs67708.2025.11283218openalex-snapshot

Dual-Ensembling for Robust Log Anomaly Detection: Combining Multi-Scale Inputs and Test-Time Augmentation

inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11464506openalex-snapshot

GTCL: Graph-Text Contrastive Learning Meets Log Anomaly Detection

inferred← citeshttps://doi.org/10.1109/ichbc68357.2025.11414591openalex-snapshot

Research on Web System Anomaly Detection and Intelligent Operations Based on Log Modeling and Self-Supervised Learning

inferred← citeshttps://doi.org/10.1109/jiot.2026.3685253openalex-snapshot

LogRMC: Robust Log Anomaly Detection in IoT Systems via Multifield Fusion, Contrastive Learning, and Pseudo-Label Refinement

inferred← citeshttps://doi.org/10.1109/saner64311.2025.00049openalex-snapshot

Cross-System Software Log-based Anomaly Detection Using Meta-Learning

inferred← citeshttps://doi.org/10.1109/tse.2025.3586700openalex-snapshot

Practitioners’ Expectations on Log Anomaly Detection

inferred← citeshttps://doi.org/10.1134/s1063779626700498openalex-snapshot

Automated Analysis of System Logs of Serial Consoles of JINR MICC Servers

inferred← citeshttps://doi.org/10.1145/3744916.3773248openalex-snapshot

Knowledge-Augmented Log Anomaly Detection with Large Language Models

inferred← citeshttps://doi.org/10.1145/3763105openalex-snapshot

Synchronized Behavior Checking: A Method for Finding Missed Compiler Optimizations

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Log-Based System Failure Prediction with Dual-Aspect Feature Augmentation

inferred← citeshttps://doi.org/10.3390/app16073208openalex-snapshot

Boundary-Aware Contrastive Learning for Log Anomaly Detection

inferred← citeshttps://doi.org/10.4018/979-8-3373-3501-8.ch008openalex-snapshot

Unsupervised, Semi-Supervised, and Self-Supervised Learning for Climate Applications

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