Fetching the paper…
Reading the bibliography…
Nowadays large computers extensively output logs to record the runtime status and it has become crucial to identify any suspicious or malicious activities from the information provided by the realtime logs.
Anomaly intrusion detection using one class SVM. In Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop, 2004. IEEE, 358–364
Yanxin Wang, Johnny Wong, and Andrew Miner. 2004 · 2004
Earlier work this paper cites.
What supercomputers say: A study of five system logs. In 37th annual IEEE/IFIP international conference on dependable systems and networks (DSN’07) . IEEE, 575–584
Adam Oliner and Jon Stearley. 2007 · 2007
Earlier work this paper cites.
Detecting large-scale system problems by mining console logs. In Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles . 117–132
Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael I Jordan. 2009 · 2009
Earlier work this paper cites.
Long short-term memory
Alex Graves and Alex Graves. 2012 · 2012
Earlier work this paper cites.
Log clustering based problem identification for online service systems. In Proceedings of the 38th International Conference on Software Engineering Companion . 102–111
Qingwei Lin, Hongyu Zhang, Jian-Guang Lou, Yu Zhang, and Xuewei Chen. 2016 · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
Earlier work this paper cites.
Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security . 1285–1298
Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar. 2017 · 2017
Earlier work this paper cites.
Drain: An online log parsing approach with fixed depth tree. In 2017 IEEE international conference on web services (ICWS) . IEEE, 33–40
Pinjia He, Jieming Zhu, Zibin Zheng, and Michael R Lyu. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Deep one-class classification. In International conference on machine learning . PMLR, 4393–4402
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018 · 2018
Cited alongside, same era.
Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs.. In IJCAI , Vol. 19. 4739–4745
Weibin Meng, Ying Liu, Yichen Zhu, Shenglin Zhang, Dan Pei, Yuqing Liu, Yihao Chen, Ruizhi Zhang, Shimin Tao, Pei Sun, et al · 2019
Cited alongside, same era.
Robust log-based anomaly detection on unstable log data. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 807–817
Xu Zhang, Yong Xu, Qingwei Lin, Bo Qiao, Hongyu Zhang, Yingnong Dang, Chunyu Xie, Xinsheng Yang, Qian Cheng, Ze Li, et al · 2019
Cited alongside, same era.
Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020 · 2020
Cited alongside, same era.
Glad-paw: Graph-based log anomaly detection by position aware weighted graph attention network. In Pacific-asia conference on knowledge discovery and data mining . Springer, 66–77
Yi Wan, Yilin Liu, Dong Wang, and Yujin Wen. 2021 · 2021
Later among the works it cites.
A2log: attentive augmented log anomaly detection
Thorsten Wittkopp, Alexander Acker, Sasho Nedelkoski, Jasmin Bogatinovski, Dominik Scheinert, Wu Fan, and Odej Kao. 2021 · 2021
Later among the works it cites.
Semi-supervised log-based anomaly detection via probabilistic label estimation. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 1448–1460
Lin Yang, Junjie Chen, Zan Wang, Weijing Wang, Jiajun Jiang, Xuyuan Dong, and Wenbin Zhang. 2021 · 2021
Later among the works it cites.
Log-based Anomaly Detection with Deep Learning: How Far Are We?. In 2022 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)
Van-Hoang Le and Hongyu Zhang. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Self-attentive classification-based anomaly detection in unstructured logs. In 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 1196–1201
Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker, Jorge Cardoso, and Odej Kao. 2020 · 2020
Cited alongside, same era.
Logbert: Log anomaly detection via bert. In 2021 international joint conference on neural networks (IJCNN) . IEEE, 1–8
Haixuan Guo, Shuhan Yuan, and Xintao Wu. 2021 · 2021
Cited alongside, same era.
LogFlash: Real-time streaming anomaly detection and diagnosis from system logs for large-scale software systems. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 80–90
Tong Jia, Yifan Wu, Chuanjia Hou, and Ying Li. 2021 · 2021
Cited alongside, same era.
Log-based anomaly detection without log parsing. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 492–504
Van-Hoang Le and Hongyu Zhang. 2021 · 2021
Cited alongside, same era.
Date: Detecting anomalies in text via self-supervision of transformers
Andrei Manolache, Florin Brad, and Elena Burceanu. 2021 · 2021
Cited alongside, same era.
LogGD: Detecting Anomalies from System Logs with Graph Neural Networks. In 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) . IEEE, 299–310
Yongzheng Xie, Hongyu Zhang, and Muhammad Ali Babar. 2022 · 2022
Later among the works it cites.
DeepTraLog: Trace-log combined microservice anomaly detection through graph-based deep learning. In Proceedings of the 44th International Conference on Software Engineering . 623–634
Chenxi Zhang, Xin Peng, Chaofeng Sha, Ke Zhang, Zhenqing Fu, Xiya Wu, Qingwei Lin, and Dongmei Zhang. 2022 · 2022
Later among the works it cites.
Lanobert: System log anomaly detection based on bert masked language model
Yukyung Lee, Jina Kim, and Pilsung Kang. 2023 · 2023
Later among the works it cites.
Glad: Content-aware dynamic graphs for log anomaly detection
Yufei Li, Yanchi Liu, Haoyu Wang, Zhengzhang Chen, Wei Cheng, Yuncong Chen, Wenchao Yu, Haifeng Chen, and Cong Liu. 2023 · 2023
Later among the works it cites.
Loggpt: Exploring chatgpt for log-based anomaly detection
Jiaxing Qi, Shaohan Huang, Zhongzhi Luan, Carol Fung, Hailong Yang, and Depei Qian. 2023 · 2023
Later among the works it cites.