Difference between revisions of "Anomaly Detection"
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** [[Risk, Compliance and Regulation]] | ** [[Risk, Compliance and Regulation]] | ||
*** [[Cybersecurity]] | *** [[Cybersecurity]] | ||
| + | **** [[Cybersecurity References]] | ||
| + | **** [[Offense - Adversarial Threats/Attacks]] | ||
| + | **** [[Defenses Against Adversarial Attacks]] | ||
** [[Government Services]] | ** [[Government Services]] | ||
* [[...find outliers]] | * [[...find outliers]] | ||
* [[Capabilities]] | * [[Capabilities]] | ||
| − | * [[ | + | * [[Internet of Things (IoT)]] |
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* [http://arxiv.org/abs/1906.03821 Time-Series Anomaly Detection Service at Microsoft | H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang] | * [http://arxiv.org/abs/1906.03821 Time-Series Anomaly Detection Service at Microsoft | H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang] | ||
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Revision as of 06:42, 22 September 2020
Youtube search... ...Google search
- Case Studies
- ...find outliers
- Capabilities
- Internet of Things (IoT)
- Time-Series Anomaly Detection Service at Microsoft | H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang
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Principal Component Analysis (PCA) Anomaly Detection
PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect Keep it Simple : Machine Learning & Algorithms for Big Boys | Dinesh Chandrasekar
