COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Can machine learning be secure?
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
Machine learning in intrusion detection
Machine learning in intrusion detection
Evading network anomaly detection systems: formal reasoning and practical techniques
Proceedings of the 13th ACM conference on Computer and communications security
Collaborative Intrusion Prevention
WETICE '07 Proceedings of the 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
Active learning with statistical models
Journal of Artificial Intelligence Research
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Undermining an anomaly-based intrusion detection system using common exploits
RAID'02 Proceedings of the 5th international conference on Recent advances in intrusion detection
The security of machine learning
Machine Learning
Learning classifiers for misuse detection using a bag of system calls representation
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Paragraph: thwarting signature learning by training maliciously
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
Least squares quantization in PCM
IEEE Transactions on Information Theory
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Active learning has played an important role in many areas because it can reduce human efforts by just selecting most informative instances for training. Nevertheless, active learning is vulnerable in adversarial environments, including intrusion detection or spam filtering. The purpose of this paper was to reveal how active learning can be attacked in such environments. In this paper, three contributions were made: first, we analyzed the sampling vulnerability of active learning; second, we presented a game framework of attack against active learning; third, two sampling attack methods were proposed, including the adding attack and the deleting attack. Experimental results showed that the two proposed sampling attacks degraded sampling efficiency of naive-bayes active learner.