Real-time detection and classification of anomalous events in streaming data
Patent
·
OSTI ID:1247988
A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- Assignee:
- UT-Battelle, LLC (Oak Ridge, TN)
- Patent Number(s):
- 9,319,421
- Application Number:
- 14/053,248
- OSTI ID:
- 1247988
- Country of Publication:
- United States
- Language:
- English
Anomaly detection: A survey
|
journal | July 2009 |
Integration of Self-Organizing Map (SOM) and Kernel Density Estimation (KDE) for network intrusion detection
|
conference | September 2009 |
VAST Challenge 2012: Visual analytics for big data
|
conference | October 2012 |
An Intrusion-Detection Model
|
journal | February 1987 |
Similar Records
Detection of anomalous events
Compression Analytics for Classification and Anomaly Detection within Network Communication
ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network
Patent
·
Tue Jun 07 00:00:00 EDT 2016
·
OSTI ID:1255959
Compression Analytics for Classification and Anomaly Detection within Network Communication
Journal Article
·
Fri Oct 26 00:00:00 EDT 2018
· IEEE Transactions on Information Forensics and Security
·
OSTI ID:1485466
ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network
Journal Article
·
Sun Jul 31 20:00:00 EDT 2022
· Int.J.Inf.Secur.
·
OSTI ID:1867680