An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Mimicry attacks on host-based intrusion detection systems
Proceedings of the 9th ACM conference on Computer and communications security
A statistical approach to the spam problem
Linux Journal
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Diagnosing network-wide traffic anomalies
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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Proceedings of the 13th ACM conference on Computer and communications security
L-diversity: Privacy beyond k-anonymity
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New Efficient Attacks on Statistical Disclosure Control Mechanisms
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Advanced allergy attacks: does a corpus really help
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A firm foundation for private data analysis
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Privacy-preserving statistical estimation with optimal convergence rates
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Differentially Private Empirical Risk Minimization
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Allergy attack against automatic signature generation
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Paragraph: thwarting signature learning by training maliciously
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
Multiple classifier systems under attack
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Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.