An active learning based TCM-KNN algorithm for supervised network intrusion detection

@article{Li2007AnAL,
  title={An active learning based TCM-KNN algorithm for supervised network intrusion detection},
  author={Yang Li and Li Guo},
  journal={Comput. Secur.},
  year={2007},
  volume={26},
  pages={459-467}
}
  • Y. Li, Li Guo
  • Published 2007
  • Computer Science
  • Comput. Secur.
As network attacks have increased in number and severity over the past few years, intrusion detection is increasingly becoming a critical component of secure information systems and supervised network intrusion detection has been an active and difficult research topic in the field of intrusion detection for many years. [...] Key Method It can effectively detect anomalies with high detection rate, low false positives under the circumstance of using much fewer selected data as well as selected features for…Expand
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References

SHOWING 1-10 OF 34 REFERENCES
Network anomaly detection based on TCM-KNN algorithm
TLDR
A novel network anomaly detection method based on improved TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) machine learning algorithm that can effectively detect anomalies with high true positive rate, low false positive rate and high confidence than the state-of-the-art anomaly detection methods. Expand
An Efficient Network Anomaly Detection Scheme Based on TCM-KNN Algorithm and Data Reduction Mechanism
  • Y. Li, Li Guo
  • Computer Science
  • 2007 IEEE SMC Information Assurance and Security Workshop
  • 2007
TLDR
The proposed novel data mining scheme for network anomaly detection can effectively detect anomalies with high detection rates, low false positives as well as with high confidence than the state-of-the-art anomaly detection methods. Expand
Detecting Novel Network Intrusions Using Bayes Estimators
TLDR
This work has been funded by AFRL Rome Labs under the contract F 30602-00-2-0512 and aims to detect well-known attacks as well as slight variations of them, by characterizing the rules that govern these attacks. Expand
Improving Intrusion Detection Performance using Keyword Selection and Neural Networks
TLDR
This approach was used to improve the baseline keyword intrusion detection system used to detect user-to-root attacks in the 1998 DARPA Intrusion Detection Evaluation, reducing the false-alarm rate required to obtain 80% correct detections by two orders of magnitude. Expand
A Study in Using Neural Networks for Anomaly and Misuse Detection
TLDR
New process-based intrusion detection approaches are described that provide the ability to generalize from previously observed behavior to recognize future unseen behavior and can be used for both anomaly detection and misuse detection. Expand
Learning nonstationary models of normal network traffic for detecting novel attacks
TLDR
This paper proposes a learning algorithm that constructs models of normal behavior from attack-free network traffic that can be combined to increase coverage of traditional intrusion detection systems. Expand
Intrusion Detection : Support Vector Machines and Neural Networks
This paper concerns intrusion detection and audit trail reduction. We describe approaches to intrusion detection and audit data reduction using support vector machines and neural networks. Using aExpand
Data Mining Approaches for Intrusion Detection
TLDR
An agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents is proposed. Expand
A framework for constructing features and models for intrusion detection systems
TLDR
A novel framework, MADAM ID, for Mining Audit Data for Automated Models for Instrusion Detection, which uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns. Expand
ADAM: Detecting Intrusions by Data Mining
TLDR
The design and experiences with the ADAM ( Audit Data Analysis and Mining) system is described, which is used as a testbed to study how useful data mining techniques can be in intrusion detection. Expand
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