Difference between revisions of "Offense - Adversarial Threats/Attacks"

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== References ==
 
 
Abadi, M. Chu , A. Goodfellow, I. McMahan, H. Mironov, I. Talwar, K. and Zhang, L. [http://arxiv.org/pdf/1607.00133.pdf Deep Learning with Differential Privacy], 24 Oct 2016
 
 
Abhijith [http://abhijith.live/introduction-to-artificial-intelligence-for-security-professionals-book/ Introduction to Artificial intelligence for security professionals], 12 Aug 2017
 
 
Abramson, Myriam [http://pdfs.semanticscholar.org/b2f7/69ddcf8cae594f39e839aa29b27b98f403ca.pdf Toward Adversarial Online Learning and the Science of Deceptive Machines], 13 Sep 2017
 
 
Akhtar et al. [http://arxiv.org/pdf/1801.00553.pdf Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey], 26 Feb 2018
 
 
Al-Dujaili et al. [http://arxiv.org/pdf/1801.02950.pdf Adversarial Deep Learning for Robust Detection of Binary Encoded Malware], 25 Mar 2018
 
 
Amodei, D. and Olah, C. et al. [http://arxiv.org/pdf/1606.06565.pdf Concrete Problems in AI Safety], 25 Jul 2016
 
 
Anderson, H.S., Kharkar, A., Filar, B., Evans, D., and Roth, P. [http://arxiv.org/pdf/1801.08917.pdf Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning], 26 Jan 2018
 
 
Anderson, H., Woodbridge, J., and Filar, B. [http://arxiv.org/pdf/1610.01969.pdf DeepDGA: Adversarially-Tuned Domain Generation and Detection], 6 Oct 2016
 
 
Arulkumaran, K., Deisenroth, M., Brundage, M., and Bharath, A. [http://arxiv.org/pdf/1708.05866.pdf A Brief Survey of Deep Reinforcement Learning], 28 Sep 2017
 
 
Barreno et al. [http://bnrg.cs.berkeley.edu/~adj/publications/paper-files/asiaccs06.pdf Can Machine Learning Be Secure?], 21 Mar 2016
 
 
Bastani, O., Kim, C., Bastani [http://arxiv.org/pdf/1705.08504.pdf Interpreting Blackbox Models via Model Extraction], 22 May 2018
 
 
Biggio, B., Nelson, B., and P. Laskov [http://arxiv.org/pdf/1206.6389.pdf Poisoning Attacks against Support Vector Machines], 25 Mar 2013
 
 
Biggio et al. [http://proceedings.mlr.press/v20/biggio11/biggio11.pdf Support Vector Machines Under Adversarial Label Noise], 2011
 
 
Brundage et al. [http://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation], Feb 2018
 
 
Bulo et al. [http://pralab.diee.unica.it/sites/default/files/bulo16-tnnls.pdf Randomized Prediction Games for Adversarial Machine Learning], 11 Nov 2017
 
 
Carbon Black [http://www.carbonblack.com/wp-content/uploads/2017/03/Carbon_Black_Research_Report_NonMalwareAttacks_ArtificialIntelligence_MachineLearning_BeyondtheHype.pdf Beyond the Hype: Security Experts
 
Weigh in on Artificial Intelligence, Machine Learning, and Non-Malware Attacks], 2017
 
 
Carlini et al. [http://arxiv.org/pdf/1801.01944.pdfAudio Adversarial Examples: Targeted Attacks on Speech-to-Text], 5 Jan 2018
 
 
Chen et al. [http://arxiv.org/pdf/1706.04146.pdf Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach], 31 Oct 2017
 
 
Chen et al. [http://arxiv.org/pdf/1709.04114.pdf EAD. Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples], 10 Feb 2018
 
 
Chen et al. [http://www.researchgate.net/publication/317576889_Hardening_Malware_Detection_Systems_Against_Cyber_Maneuvers_An_Adversarial_Machine_Learning_Approach Hardening Malware Detection Systems Against Cyber Maneuvers. An Adversarial Machine Learning Approach], 13 Oct 2017
 
 
Chen et al. [http://arxiv.org/pdf/1712.05526.pdf Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning], 15 Dec 2017
 
 
Demontis et al. [http://ceur-ws.org/Vol-1816/paper-11.pdf Infinity-Norm Support Vector Machines Against Adversarial Label Contamination], 2017
 
 
Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T., Li, B., Prakash, A., Rahmati, A., and Song, D. [http://arxiv.org/pdf/1707.08945.pdf Robust Physical-World Attacks on Deep Learning Visual Classification], 2017
 
 
Goodfellow et al. [http://arxiv.org/pdf/1607.02533.pdf Adversarial examples in the physical world], 11 Feb 2017
 
 
Goodfellow et al. [http://arxiv.org/pdf/1802.08195.pdf Adversarial Examples that Fool both Human and Computer Vision], 22 May 2018
 
 
Goodfellow et al. [http://blog.openai.com/adversarial-example-research/ Attacking Machine Learning with Adversarial Examples], 24 Feb 2017
 
 
Goodfellow et al. [http://arxiv.org/pdf/1412.6572.pdf Explaining and Harnessing Adversarial Examples], 20 Mar 2015
 
 
Goodfellow et al. [http://arxiv.org/pdf/1312.6199.pdf Intriguing properties of neural networks], 19 Feb 2014
 
 
Goodfellow et al. [http://arxiv.org/pdf/1704.03453.pdf The Space of Transferable Adversarial Examples], 23 May 2017
 
 
Goodfellow et al. [http://arxiv.org/pdf/1605.07277.pdf Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples], 24 May 2016
 
 
Grosse et al. [http://www.patrickmcdaniel.org/pubs/esorics17.pdf Adversarial Examples for Malware Detection], 12 Aug 2017
 
 
Grosse et al. [http://arxiv.org/pdf/1606.04435.pdf Adversarial Perturbations Against Deep Neural Networks for Malware Classification], 16 Jun 2016
 
 
Hosseini et al. [http://arxiv.org/pdf/1703.04318.pdf Blocking Transferability of Adversarial Examples in Black-Box Learning Systems], 13 Mar 2017
 
 
Hu et al. [http://arxiv.org/pdf/1702.05983.pdf Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN], 20 Feb 2017
 
 
Huang et al. [http://people.eecs.berkeley.edu/~tygar/papers/SML2/Adversarial_AISEC.pdf Adversarial Machine Learning], Oct 2011
 
 
Jin et al. [http://arxiv.org/pdf/1511.06306.pdf Robust Convolutional Neural Networks under Adversarial Noise], 25 Feb 2016
 
 
Kantarcioglu et al. [http://www.utdallas.edu/~muratk/CCS-tutorial.pdf Adversarial Data Mining for Cyber Security], 28 Oct 2016
 
 
Kantchelian et al. [https://arxiv.org/pdf/1509.07892.pdf Evasion and Hardening of Tree Ensemble Classifiers], 27 May 2016
 
 
Kantchelian [http://pdfs.semanticscholar.org/4a8d/97172382144b9906e2cec69d3decb4188fb7.pdf Taming Evasions in Machine Learning Based Detection], 12 Aug 2016
 
 
Keshet et al. [http://www.groundai.com/project/adversarial-examples-on-discrete-sequences-for-beating-whole-binary-malware-detection/ Adversarial Examples on Discrete Sequences for Beating Whole-Binary Malware Detection], 13 Feb 2018
 
 
Kolosnjaji et al. [http://arxiv.org/pdf/1803.04173.pdf Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables], 12 Mar 2018
 
 
Kreuk et al. [http://arxiv.org/pdf/1802.04528.pdf Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples], 13 May 2018
 
 
Laskov et al. [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.4564&rep=rep1&type=pdf Machine Learning in Adversarial Environments], 28 Jun 2010
 
 
Luo et al. [http://arxiv.org/pdf/1801.04693.pdf Towards Imperceptible and Robust Adversarial Example Attacks against Neural], 15 Jan 2018
 
 
Madry et al. [http://arxiv.org/pdf/1706.06083.pdf Towards Deep Learning Models Resistant to Adversarial Attacks], 19 Jun 2017
 
 
Miller et al. [http://arxiv.org/pdf/1705.09823.pdf Adversarial Learning. A Critical Review and Active Learning Study], 27 May 2017
 
 
Muñoz-González et al. [http://arxiv.org/pdf/1708.08689.pdf Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization], 29 Aug 2017
 
 
Naveiro et al. [http://arxiv.org/pdf/1802.07513.pdf Adversarial classification: An adversarial risk analysis approach], 21 Feb 2018
 
 
Nguyen et al. [http://arxiv.org/pdf/1412.1897.pdf Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images], 2 Apr 2015
 
 
Norton et al. [http://arxiv.org/pdf/1708.00807.pdf Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning], 1 Aug 2017
 
 
Ororbia II et al. [http://arxiv.org/pdf/1601.07213.pdf Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization], 29 Jul 2016
 
 
Papernot et al. [http://www.usenix.org/sites/default/files/conference/protected-files/enigma17_slides_papernot.pdf  Adversarial Examples in Machine Learning], 1 Feb 2017
 
 
Papernot et al. [http://arxiv.org/pdf/1606.04435.pdf Adversarial Perturbations Against Deep Neural Networks], 16 Jun 2016
 
 
Papernot et al. [http://arxiv.org/pdf/1511.04508.pdf Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks], 14 Nov 2015
 
 
Papernot et al. [http://arxiv.org/abs/1511.07528 The Limitations of Deep Learning in Adversarial Settings], 24 Nov 2015
 
 
Papernot et al. [http://arxiv.org/pdf/1702.06280.pdf On the (Statistical) Detection of Adversarial Examples], 21 Feb 2017
 
 
Papernot et al. [http://arxiv.org/pdf/1602.02697.pdf Practical Black-Box Attacks against Machine Learning], 8 Feb 2016
 
 
Paudice et al. [http://arxiv.org/pdf/1802.03041.pdf Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection], 8 Feb 2018
 
 
Raghunathan et al. [http://arxiv.org/pdf/1801.09344.pdf Certified Defenses against Adversarial Examples], 29 Jan 2018
 
 
Rouhani et al. [http://arxiv.org/pdf/1709.02538.pdf CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning], 1 Apr 2018
 
 
Shen et al. [http://www.comp.nus.edu.sg/~shruti90/papers/auror.pdf AUROR: Defending Against Poisoning Attacks in Collaborative Deep Learning Systems], 5 Dec 2016
 
 
Stokes et al. [http://arxiv.org/pdf/1712.05919.pdf Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models] 16 Dec 2017
 
 
Uesato et al. [http://arxiv.org/pdf/1802.05666.pdf Adversarial Risk and the Dangers of Evaluating Against Weak Attacks], 12 Jun 2018
 
 
Uther et al.[http://www.cs.cmu.edu/~mmv/papers/03TR-advRL.pdf Adversarial Reinforcement Learning], Jan 2003
 
 
Wang et al. [http://arxiv.org/pdf/1610.01239.pdf Adversary Resistant Deep Neural Networks with an Application to Malware Detection], 27 Apr 2017
 
 
Xiao et al. [http://pdfs.semanticscholar.org/6adb/6154e091e6448d63327eadb6159746a2710d.pdf Adversarial and Secure Machine Learning], 27 Oct 2016
 
 
Xu et al. [http://evademl.org/docs/evademl.pdf Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers], Feb 2016
 
 
Xu et al. [http://arxiv.org/pdf/1704.01155.pdf Feature Squeezing; Detecting Adversarial Examples in Deep Neural Networks], 5 Dec 2017
 
 
Yuan et al. [http://arxiv.org/pdf/1712.07107.pdf Adversarial Examples. Attacks and Defenses for Deep Learning], 5 2018]
 
  
 
== Weaponizing Machine Learning ==  
 
== Weaponizing Machine Learning ==  

Revision as of 08:25, 28 June 2018

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Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. Myth: An attacker must have access to the model to generate adversarial examples. Fact: Adversarial examples generalize across models trained to perform the same task, even if those models have different architectures and were trained on a different training set. This means an attacker can train their own model, generate adversarial examples against it, and then deploy those adversarial examples against a model they do not have access to. -Deep Learning Adversarial Examples – Clarifying Misconceptions | Goodfellow et al.


Weaponizing Machine Learning

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