Competitive Advantages
- Monitors and analyzes large data sets
- Accurate detection of irregularities
- Minimized average detection delay
Summary
We have developed a technique called Online Discrepancy Test (ODIT) for real-time detection of anomalies in high-dimensional systems. This algorithm is generic and applicable to various contexts as it does not assume specific data types, probability distributions, and anomaly types. It only requires a nominal training set, and achieves asymptotic optimality in terms of minimizing average detection delay for a given false alarm constraint. Due to its multivariate nature, it can quickly and accurately detect challenging anomalies, such as changes in the correlation structure and stealth low -rate cyberattacks. In conjunction with the detection method there is also an effective technique for localizing the anomalous data dimensions.
![](https://usf-test.testtechnologypublisher.com/files/sites/20a0171.png)
Performance of ODIT for an Unknown Anomaly Type in all Three Versions
Desired Partnerships
- License
- Sponsored Research
- Co-Development