Difference between revisions of "Anomaly Detection"
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**** [[Offense - Adversarial Threats/Attacks]] | **** [[Offense - Adversarial Threats/Attacks]] | ||
**** [[Defenses Against Adversarial Attacks]] | **** [[Defenses Against Adversarial Attacks]] | ||
| − | + | * [[Government Services]] | |
| + | ** [[National Institute of Standards and Technology (NIST)]] | ||
| + | ** [[U.S. Department of Homeland Security (DHS)]] | ||
| + | ** [[Defense]] | ||
* [[...find outliers]] | * [[...find outliers]] | ||
* [[Capabilities]] | * [[Capabilities]] | ||
Revision as of 07:03, 5 May 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Case Studies
- Government Services
- ...find outliers
- Capabilities
- Embedding: Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction ... ...find outliers
- Internet of Things (IoT)
- Screening; Passenger, Luggage, & Cargo
- 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
Anomalies data points which do not conform to an expected pattern of the other items in the data set
Anomaly Detection. Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different.
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
