Mining adversarial patterns via regularized loss minimization

Liu, Wei; Chawla, Sanjay

doi:10.1007/s10994-010-5199-2

Cited by 50 publications

(46 citation statements)

References 16 publications

(23 reference statements)

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“…Later on such an equilibrium is used to choose optimal set of attributes that give good equilibrium performance. Improved models in which Nash strategies are played have also been proposed [4,18].…”

Section: Related Workmentioning

confidence: 99%

Adversarial support vector machine learning

Zhou¹,

Kantarcıoğlu²,

Thuraisingham³

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

103

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Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack strategy and develop robust learning models to mitigate the attack. These are the two objectives of this paper. We consider two attack models: a free-range attack model that permits arbitrary data corruption and a restrained attack model that anticipates more realistic attacks that a reasonable adversary would devise under penalties. We then develop optimal SVM learning strategies against the two attack models. The learning algorithms minimize the hinge loss while assuming the adversary is modifying data to maximize the loss. Experiments are performed on both artificial and real data sets. We demonstrate that optimal solutions may be overly pessimistic when the actual attacks are much weaker than expected. More important, we demonstrate that it is possible to develop a much more resilient SVM learning model while making loose assumptions on the data corruption models. When derived under the restrained attack model, our optimal SVM learning strategy provides more robust overall performance under a wide range of attack parameters.

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Section: Related Workmentioning

confidence: 99%

Adversarial support vector machine learning

Zhou¹,

Kantarcıoğlu²,

Thuraisingham³

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

103

View full text Add to dashboard Cite

show abstract

“…They assume the two players know each other's payoff function. Similar work with improvement on how Nash strategies are played has also been proposed [8], [9]. Brückner and Scheffer [10] present an optimal game by assuming the adversaries always behave rationally.…”

Section: Related Workmentioning

confidence: 99%

Sparse Bayesian Adversarial Learning Using Relevance Vector Machine Ensembles

Zhou

Kantarcıoğlu

Thuraisingham

2012

2012 IEEE 12th International Conference on Data Mining

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Abstract-Data mining tasks are made more complicated when adversaries attack by modifying malicious data to evade detection. The main challenge lies in finding a robust learning model that is insensitive to unpredictable malicious data distribution. In this paper, we present a sparse relevance vector machine ensemble for adversarial learning. The novelty of our work is the use of individualized kernel parameters to model potential adversarial attacks during model training. We allow the kernel parameters to drift in the direction that minimizes the likelihood of the positive data. This step is interleaved with learning the weights and the weight priors of a relevance vector machine. Our empirical results demonstrate that an ensemble of such relevance vector machine models is more robust to adversarial attacks.

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“…However, in many situations there exists an adversary (such as a spammer) who manipulates the training data distribution (e.g., spam emails) in a way as to attack the classifiers (spam detectors). This is a scenario that challenges the assumption made in most traditional classifiers, and thus has motivated the research advanced of adversarial learning [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [4] formulated the interaction between a data miner and an adversary as a Zero-sum game, where the adversary is the leader and the data miner is the follower. However, the Zero-sum game indicates that the model assumes the adversary is being antagonistic against the data miner.…”

Section: Introductionmentioning

confidence: 99%

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On Sparse Feature Attacks in Adversarial Learning

Wang

Liu

Chawla

2014

2014 IEEE International Conference on Data Mining

Self Cite

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Adversarial learning is the study of machine learning techniques deployed in non-benign environments. Example applications include classifications for detecting spam email, network intrusion detection and credit card scoring. In fact as the gamut of application domains of machine learning grows, the possibility and opportunity for adversarial behavior will only increase.Till now, the standard assumption about modeling adversarial behavior has been to empower an adversary to change all features of the classifiers at will. The adversary pays a cost proportional to the size of "attack". We refer to this form of adversarial behavior as a dense feature attack.However, the aim of an adversary is not just to subvert a classifier but carry out data transformation in a way such that spam continues to appear like spam to the user as much as possible. We demonstrate that an adversary achieves this objective by carrying out a sparse feature attack. We design an algorithm to show how a classifier should be designed to be robust against sparse adversarial attacks. Our main insight is that sparse feature attacks are best defended by designing classifiers which use 1 regularizers.

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Mining adversarial patterns via regularized loss minimization

Cited by 50 publications

References 16 publications

Adversarial support vector machine learning

Adversarial support vector machine learning

Sparse Bayesian Adversarial Learning Using Relevance Vector Machine Ensembles

On Sparse Feature Attacks in Adversarial Learning

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