In-situ trainable intrusion detection system
Abstract
A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.
- Inventors:
- Issue Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1332095
- Patent Number(s):
- 9,497,204
- Application Number:
- 14/468,000
- Assignee:
- UT-Battelle, LLC (Oak Ridge, TN) ORNL
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2014 Aug 25
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS
Citation Formats
Symons, Christopher T., Beaver, Justin M., Gillen, Rob, and Potok, Thomas E. In-situ trainable intrusion detection system. United States: N. p., 2016.
Web.
Symons, Christopher T., Beaver, Justin M., Gillen, Rob, & Potok, Thomas E. In-situ trainable intrusion detection system. United States.
Symons, Christopher T., Beaver, Justin M., Gillen, Rob, and Potok, Thomas E. Tue .
"In-situ trainable intrusion detection system". United States. https://www.osti.gov/servlets/purl/1332095.
@article{osti_1332095,
title = {In-situ trainable intrusion detection system},
author = {Symons, Christopher T. and Beaver, Justin M. and Gillen, Rob and Potok, Thomas E.},
abstractNote = {A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {11}
}