Communications of the ACM
Machine Learning
Machine Learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Can machine learning be secure?
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
Adaptive communal detection in search of adversarial identity crime
Proceedings of the 2007 international workshop on Domain driven data mining
Exploiting machine learning to subvert your spam filter
LEET'08 Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
The Journal of Machine Learning Research
Partitioned logistic regression for spam filtering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Open problems in the security of learning
Proceedings of the 1st ACM workshop on Workshop on AISec
Adversarial Pattern Classification Using Multiple Classifiers and Randomisation
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Ensemble Based Data Fusion for Gene Function Prediction
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Online phishing classification using adversarial data mining and signaling games
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
ANTIDOTE: understanding and defending against poisoning of anomaly detectors
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Online phishing classification using adversarial data mining and signaling games
ACM SIGKDD Explorations Newsletter
A misleading attack against semi-supervised learning for intrusion detection
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Classifier evaluation and attribute selection against active adversaries
Data Mining and Knowledge Discovery
Filtering artificial texts with statistical machine learning techniques
Language Resources and Evaluation
Proceedings of the 4th Workshop on Social Network Systems
Classifier evasion: models and open problems
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
Detecting adversarial advertisements in the wild
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 4th ACM workshop on Security and artificial intelligence
Understanding the risk factors of learning in adversarial environments
Proceedings of the 4th ACM workshop on Security and artificial intelligence
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
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Key challenges in defending against malicious socialbots
LEET'12 Proceedings of the 5th USENIX conference on Large-Scale Exploits and Emergent Threats
Domain adaptation with ensemble of feature groups
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Adversarial support vector machine learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Evasion attack of multi-class linear classifiers
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Query strategies for evading convex-inducing classifiers
The Journal of Machine Learning Research
Sampling attack against active learning in adversarial environment
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
An efficient adversarial learning strategy for constructing robust classification boundaries
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Design and analysis of a social botnet
Computer Networks: The International Journal of Computer and Telecommunications Networking
An agent-based model to simulate and analyse behaviour under noisy and deceptive information
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
On the hardness of evading combinations of linear classifiers
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2]. In this paper, we introduce the adversarial classifier reverse engineering (ACRE) learning problem, the task of learning sufficient information about a classifier to construct adversarial attacks. We present efficient algorithms for reverse engineering linear classifiers with either continuous or Boolean features and demonstrate their effectiveness using real data from the domain of spam filtering.