A Game Theoretical Model for Adversarial Learning

By Sanjay Chawla. 2009

73 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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Citations in the corpus

Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners (2015, background): “Introduction The study on security threats to intelligent agents has a long history (Nelson et al. 2009; Tan, Killourhy, and Maxion 2002; Rubinstein et al. 2008; Barreno et al. 2010; Laskov and Lippmann 2010; Barreno et al. 2006; Dalvi et al. 2004; Liu and Chawla 2009; Biggio, Fumera, and Roli 2013;”
When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks (2018, background): “Furthermore, [27, 7] introduce game theoretical Stackelberg formulations for the interaction between the adversary and the data miner in the case of data manipulations.”
Data Poisoning Attacks against Autoregressive Models (2016, background): “A somewhat orthogonal line of research has posed the problem of learning in the presence of adversaries in a game theoretic context (Liu and Chawla 2009; Brückner, Kanzow, and Scheffer 2012; Dalvi et al. 2004; Brückner and Scheffer 2009; 2011).”
Protecting location privacy: optimal strategy against localization attacks (2012, methodology): “A similar approach is used by Liu and Chawla [20] in the design of an optimal e-mail spam ﬁlter, taking into account that spammers adapt their e-mails to get past the spam detectors.”
Unsupervised Learning Trojan (2014, methodology): “A different approach to Nelson, Rubinstein, et al. is explored in W. Liu & Chawla (2009), where ACRE is used under the assumption that adversarial machine learning is a Stackelberg game.”
Sparse Feature Attacks in Adversarial Learning (2014, unknown): “[32] 0.”
Feature Cross-Substitution in Adversarial Classification (2014, background): “learner and adversary as players in a game in which the learner chooses a classifier or a learning algorithm, and the attacker modifies either the training or test data [13, 14, 15, 16, 8, 17, 18].”
Some Submodular Data-Poisoning Attacks on Machine Learners (2015, background): “The security community has long recognized the threats of data-poisoning attacks (a.k.a. causative attacks) on machine learning systems [1–6,9,10,12,16], where an attacker modiﬁes the training data, so that the learning algorithm arrives at a “wrong” model that is useful to the attacker.”
Stackelberg games for adversarial prediction problems (2011, background): “Some authors [9, 12] study the case in which the data generator acts as leader and the learner as follower.”
Explicit Defense Actions Against Test-Set Attacks (2017, background): “A separate line of research has posed the problem of learning in the presence of adversaries in game theoretic contexts (Liu and Chawla 2009; Brückner, Kanzow, and Scheffer 2012; Dalvi et al. 2004; Brückner and Scheffer 2009; 2011; Hardt et al. 2016; Brückner and Scheffer 2011).”