Context‐Aware Learning for Robust Anomaly Detection *
Author:
Publisher
Wiley
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119835905.ch7
Reference43 articles.
1. ChristopheBertero MatthieuRoy CarlaSauvanaud andGillesTrédan.Experience report: Log mining using natural language processing and application to anomaly detection. In28th IEEE International Symposium on Software Reliability Engineering ISSRE 2017 Toulouse France October 23–26 2017 pages351–360. IEEE Computer Society 2017. doi: 10.1109/ISSRE.2017.43.
2. AndyBrown AaronTuor BrianHutchinson andNicoleNichols.Recurrent neural network attention mechanisms for interpretable system log anomaly detection. InProceedings of the 1st Workshop on Machine Learning for Computing Systems MLCS'18 New York NY USA 2018. Association for Computing Machinery. ISBN 9781450358651. doi: 10.1145/3217871.3217872.
3. HanlinCao HainaTang YuleiWu FeiWang andYongjunXu. On accurate computation of trajectory similarity via single image super‐resolution. In2021 International Joint Conference on Neural Networks (IJCNN) pages1–9 2021. doi: 10.1109/IJCNN52387.2021.9533802.
4. SMOTE: Synthetic Minority Over-sampling Technique
5. MinDu FeifeiLi GuinengZheng andVivekSrikumar.DeepLog: Anomaly detection and diagnosis from system logs through deep learning. InProceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security CCS '17 pages1285–1298 New York NY USA 2017. Association for Computing Machinery. ISBN 9781450349468. doi: 10.1145/3133956.3134015.
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