In-situ trainable intrusion detection system
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.
- 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):
- 9,497,204
- Application Number:
- 14/468,000
- OSTI ID:
- 1332095
- Resource Relation:
- Patent File Date: 2014 Aug 25
- Country of Publication:
- United States
- Language:
- English
Similar Records
Profile-based adaptive anomaly detection for network security.
Network Anomaly Detection Using Federated Learning