Making Systems Fail-Aware: A Semi-Supervised Machine Learning Approach for Identifying Failures by Learning the Correct Behavior of a System

Author:

Muehlburger Herbert,Wotawa Franz

Publisher

Elsevier BV

Reference19 articles.

1. Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks. In T.K. Dang, R. Wagner, J. Küng, N. Thoai, M. Takizawa, and E. Neuhold (eds.), Future Data and Security Engineering, volume 10018, 141–152;Bontemps,2016

2. A security monitoring system for internet of things;Casola;Internet of Things,2019

3. Chalapathy, R. and Chawla, S. (2019). Deep Learning for Anomaly Detection: A Survey. arXiv:1901.03407 [cs, stat].

4. Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 15:1–15:58.

5. Speech recognition with deep recurrent neural networks;Graves;In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing,2013

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