DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
By Min Du, Feifei Li, Guineng Zheng, Vivek Srikumar. Conference on Computer and Communications Security, 2017
1,955 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/fefeded74334e5eafa47c5df6de2837fe3b7502d
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Anton van den Hengel
- Chunhua Shen
- Hongyu Zhang
- Van-Hoang Le
- Shuhan Yuan
- Guansong Pang
- Mohamed Hammad
- Mohamed Jaward Bah
In Computer Networks and Communications
- Hongyu Zhang
- Van-Hoang Le
- Xintao Wu
- Haixuan Guo
- Shuhan Yuan
- Michael R. Lyu
- Zibin Zheng
- Guansong Pang
Citations in the corpus
- Log-based Anomaly Detection Without Log Parsing (2021, methodology): “For example, DeepLog [5] first applies the Spell [27] parser to extract log templates.”
- Deep Learning for Anomaly Detection: A Survey (2019, methodology): “Several studies have been conducted to understand the theoretical and practical applications of deep learning in medical and bio-informatics (Min et al. [2017], Cao et al. [2018a], Zhao et al. [2016], Khan and Yairi [2018]).”
- Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy (2019, methodology): “Over the past decade this log dataset has been extensively used for research in system log anomaly detection ([15, 16, 5]).”
- Deep Learning for Anomaly Detection (2020, methodology): “ch Campus [88] 317,398 frames N/A 5.38% Video [88] Video surveillance UCF-Crime [146] 1,900 videos (13.8m frames) N/A 13 crimes Video [146] System log analysis HDFS Log [165] 11.2m N/A 2.90% Sequence [41] System log analysis OpenStack log 1.3m N/A 7.00% Sequence [41] 8 CONCLUSIONS AND FUTURE OPPORTU”
- DÏoT: A Federated Self-learning Anomaly Detection System for IoT (2018, background): “Most applications leverage Long Short-Term Memory (LSTM) networks for detecting anomalies in time series [45], aircraft data [46] or system logs [47].”
- Tools and Benchmarks for Automated Log Parsing (2018, background): “With close collaborations with our industrial partners, as well as some peer researchers (authors from [10], [18], [30]), we collect a large set of logs (over 70GB in total) produced by 16 different systems spanning distributed systems, supercomputers, operating systems, mobile systems, server…”
- Progress in Outlier Detection Techniques: A Survey (2019, background): “[216] proposed Deeplog, a universal framework that adopts a deep neural network approach for online log outlier detection and analysis.”
- LogBERT: Log Anomaly Detection via BERT (2021, positive): “Regarding baselines, we leverage the package Loglizer [4] to evaluate PCA, OCSVM, iForest as well as LogCluster for anomaly detection and adopt the open source deep learning-based log analysis toolkit to evaluate DeepLog and LogAnomaly 3 .”