Theoretically Optimal Distributed Anomaly Detection
A novel general framework for distributed anomaly detection with theoretical performance guarantees is proposed. Our algorithmic approach combines existing anomaly detection procedures with a novel method for computing global statistics using local sufficient statistics. Under a Gaussian assumption, our distributed algorithm is guaranteed to perform as well as its centralized counterpart, a condition we call Ôzero information lossÕ. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach.
Find Related Datasets
Search by Tags
Click any tag below to search for similar datasets
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[
"026:00"
]
|
| contactPoint |
{
"fn": "Nikunj Oza",
"@type": "vcard:Contact",
"hasEmail": "mailto:Nikunj.C.Oza@nasa.gov"
}
|
| description | A novel general framework for distributed anomaly detection with theoretical performance guarantees is proposed. Our algorithmic approach combines existing anomaly detection procedures with a novel method for computing global statistics using local sufficient statistics. Under a Gaussian assumption, our distributed algorithm is guaranteed to perform as well as its centralized counterpart, a condition we call Ôzero information lossÕ. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "lasr09.pdf",
"format": "PDF",
"mediaType": "application/pdf",
"description": "lasr09.pdf",
"downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/lasr09.pdf"
}
]
|
| identifier | DASHLINK_539 |
| issued | 2012-02-26 |
| keyword |
[
"ames",
"dashlink",
"nasa"
]
|
| landingPage | https://c3.nasa.gov/dashlink/resources/539/ |
| modified | 2025-03-31 |
| programCode |
[
"026:029"
]
|
| publisher |
{
"name": "Dashlink",
"@type": "org:Organization"
}
|
| title | Theoretically Optimal Distributed Anomaly Detection |
