The nature of statistical learning theory
The nature of statistical learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
The base-rate fallacy and the difficulty of intrusion detection
ACM Transactions on Information and System Security (TISSEC)
ACM Transactions on Information and System Security (TISSEC)
Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
NetSTAT: A Network-Based Intrusion Detection Approach
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Detecting Anomalous and Unknown Intrusions Against Programs
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Using Active Learning in Intrusion Detection
CSFW '04 Proceedings of the 17th IEEE workshop on Computer Security Foundations
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Serial Combination of Anomaly and Misuse IDSes Applied to HTTP Traffic
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
Snort - Lightweight Intrusion Detection for Networks
LISA '99 Proceedings of the 13th USENIX conference on System administration
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
A Hybrid Network Intrusion Detection Technique Using Random Forests
ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
Semi-supervised co-training and active learning based approach for multi-view intrusion detection
Proceedings of the 2009 ACM symposium on Applied Computing
Active learning for network intrusion detection
Proceedings of the 2nd ACM workshop on Security and artificial intelligence
Semi-Naïve Bayesian Method for Network Intrusion Detection System
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
Semi-supervised learning by disagreement
Knowledge and Information Systems
Semi-supervised learning for false alarm reduction
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
IDS false alarm reduction using continuous and discontinuous patterns
ACNS'05 Proceedings of the Third international conference on Applied Cryptography and Network Security
IDS false alarm filtering using KNN classifier
WISA'04 Proceedings of the 5th international conference on Information Security Applications
Unlabeled data and multiple views
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Improving the performance of neural networks with random forest in detecting network intrusions
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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With the development of intrusion detection systems (IDSs), a number of machine learning approaches have been applied to intrusion detection. For a traditional supervised learning algorithm, training examples with ground-truth labels should be given in advance. However, in real applications, the number of labeled examples is limited whereas a lot of unlabeled data is widely available, because labeling data requires a large amount of human efforts and is thus very expensive. To mitigate this issue, several semi-supervised learning algorithms, which aim to label data automatically without human intervention, have been proposed to utilize unlabeled data in improving the performance of IDSs. In this paper, we attempt to apply disagreement-based semi-supervised learning algorithm to anomaly detection. Based on our previous work, we further apply this approach to constructing a false alarm filter and investigate its performance of alarm reduction in a network environment. The experimental results show that the disagreement-based scheme is very effective in detecting intrusions and reducing false alarms by automatically labeling unlabeled data, and that its performance can further be improved by co-working with active learning.