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The system log generated in a computer system refers to large-scale data that are collected simultaneously and used as the basic data for determining errors, intrusion and abnormal behaviors.
Cross-lingual language model pretraining
Lample, G. and Conneau, A · 1901
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“cloze procedure”: A new tool for measuring readability
Taylor, W. L · 1953
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Automated system monitoring and notification with swatch
Hansen, S. E. and Atkins, E. T · 1993
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Estimating the support of a high-dimensional distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J. C., Smola, A. J., and Williamson, R. C · 2001
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Analyzing cluster log files using logsurfer
Prewett, J. E · 2003
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Anomaly intrusion detection using one class svm
Wang, Y., Wong, J., and Miner, A · 2004
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Availability assessment of sunos/solaris unix systems based on syslogd and wtmpx log files: A case study
Simache, C. and Kaaniche, M · 2005
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What supercomputers say: A study of five system logs
Oliner, A. and Stearley, J · 2007
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Isolation forest
Liu, F. T., Ting, K. M., and Zhou, Z.-H · 2008
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Detecting large-scale system problems by mining console logs
Xu, W., Huang, L., Fox, A., Patterson, D., and Jordan, M. I · 2009
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Event logs for the analysis of software failures: A rule-based approach
Cinque, M., Cotroneo, D., and Pecchia, A · 2012
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Beehive: Large-scale log analysis for detecting suspicious activity in enterprise networks
Yen, T.-F., Oprea, A., Onarlioglu, K., Leetham, T., Robertson, W., Juels, A., and Kirda, E · 2013
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Detection of early-stage enterprise infection by mining large-scale log data
Oprea, A., Li, Z., Yen, T.-F., Chin, S. H., and Alrwais, S · 2015
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Spell: Streaming parsing of system event logs
Du, M. and Li, F · 2016
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Kim, G., Yi, H., Lee, J., Paek, Y., and Yoon, S · 2016
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Log clustering based problem identification for online service systems
Lin, Q., Zhang, H., Lou, J.-G., Zhang, Y., and Chen, X · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al · 2016
Cited alongside, same era.
Deeplog: Anomaly detection and diagnosis from system logs through deep learning
Du, M., Li, F., Zheng, G., and Srikumar, V · 2017
Cited alongside, same era.
Drain: An online log parsing approach with fixed depth tree
He, P., Zhu, J., Zheng, Z., and Lyu, M. R · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
Cited alongside, same era.
Recurrent neural network attention mechanisms for interpretable system log anomaly detection
Brown, A., Tuor, A., Hutchinson, B., and Nichols, N · 2018
Cited alongside, same era.
Robust log-based anomaly detection on unstable log data
Zhang, X., Xu, Y., Lin, Q., Qiao, B., Zhang, H., Dang, Y., Xie, C., Yang, X., Cheng, Q., Li, Z., et al · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., and Smith, N. A · 2020
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Hitanomaly: Hierarchical transformers for anomaly detection in system log
Huang, S., Liu, Y., Fung, C., He, R., Zhao, Y., Yang, H., and Luan, Z · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., and Levy, O · 2020
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Deep one-class classification
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., and Kloft, M · 2018
Cited alongside, same era.
What does BERT look at? an analysis of BERT’s attention
Clark, K., Khandelwal, U., Levy, O., and Manning, C. D · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Parameter-efficient transfer learning for NLP
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
Cited alongside, same era.
What does BERT learn about the structure of language?
Jawahar, G., Sagot, B., and Seddah, D · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
Cited alongside, same era.
Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs
Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., Chen, Y., Zhang, R., Tao, S., Sun, P., and Zhou, R · 2019
Cited alongside, same era.
Self-attentive classification-based anomaly detection in unstructured logs
Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., and Kao, O · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
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Transformer protein language models are unsupervised structure learners
Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., and Rives, A · 2020
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Prottrans: Towards cracking the language of lifes code through self-supervised deep learning and high performance computing
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Yu, W., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., Bhowmik, D., and Rost, B · 2021
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Logbert: Log anomaly detection via bert
Guo, H., Yuan, S., and Wu, X · 2021
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Log-based anomaly detection without log parsing
Le, V.-H. and Zhang, H · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, 2021
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
Schick, T. and Schütze, H · 2021
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LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
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A unified model for multi-class anomaly detection
You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., and Le, X · 2022
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