Cybersecurity References
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Arulkumaran, K., Deisenroth, M., Brundage, M., and Bharath, A. A Brief Survey of Deep Reinforcement Learning, 28 Sep 2017
Akhtar et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey, 26 Feb 2018
Brundage et al. The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, Feb 2018
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Abadi, M. Chu , A. Goodfellow, I. McMahan, H. Mironov, I. Talwar, K. and Zhang, L. Deep Learning with Differential Privacy, 24 Oct 2016
Abhijith Introduction to Artificial intelligence for security professionals, 12 Aug 2017
Abramson, Myriam Toward Adversarial Online Learning and the Science of Deceptive Machines, 13 Sep 2017
Al-Dujaili, A., Haung, A., Hemberg, E., O'reilly, U. Adversarial Deep Learning for Robust Detection of Binary Encoded Malware, 25 Mar 2018
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American Technology Council (ATC), U.S. Government Report to the President on IT Modernization, 2017
Amodei, D. and Olah, C. et al. Concrete Problems in AI Safety, 25 Jul 2016
Anderson, H.S., Kharkar, A., Filar, B. Evading Machine Learning Malware Detection, 27 Jul 2017
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Anderson, H.S., Woodbridge, J., and Filar, B. DeepDGA: Adversarially-Tuned Domain Generation and Detection, 6 Oct 2016
Army Cyber Institute at West Point and Arizona State University The New Dogs of War: The Future of Weaponized Artificial Intelligence, 2017
Barreno, M., Nelson, B., Sears, R., Joseph, A., Tygar, J.D. Can Machine Learning Be Secure?, 21 Mar 2016
Bastani, O., Kim, C., Bastani Interpreting Blackbox Models via Model Extraction, 22 May 2018
Biggio, B., Nelson, B., Laskov, P. Poisoning Attacks against Support Vector Machines, 25 Mar 2013
Biggio, B., Nelson, B., Laskov, P. Support Vector Machines Under Adversarial Label Noise, 2011
Bulò, S., Biggio, B., Pillai, I., Pellillo, M., Roli, F. 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, N., Wagner, D. Audio Adversarial Examples: Targeted Attacks on Speech-to-Text, 5 Jan 2018
Carlini, N., Mishra, P., Vaidya, T., Zhang, Y., Sherr, M., Shields, C., Wagner, D., and Zhou, W. Hidden Voice Commands, 2016
Carlini, N., Wagner, D. MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples, 22 Nov 2017
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Chen, P., Sharma, Y., Zhang, H., Yi, J., Hsieh, C. EAD. Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples, 10 Feb 2018
Chen, S., Xue, M., Fan, L., Hao, S., Xu, L., Zhu, H., Li, Bo. Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach, 31 Oct 2017
Chen, S., Xue, M., Fan, L., Zhu, H. Hardening Malware Detection Systems Against Cyber Maneuvers. An Adversarial Machine Learning Approach, 13 Oct 2017
Chen, X., Liu, C., Li, B., Lu, K., Song, D. Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning, 15 Dec 2017
Conroy, N. Rubin V. Chen, Y. Automatic Deception Detection: Methods for Finding Fake News, Aug 2017
Crawford, K. and Calo, R. There is a blind spot in AI research, 20 Oct 2016
D’Avino, D., Cozzolino, D., Poggi, G., and Verdoliva, L. Autoencoder with recurrent neural networks for video forgery detection, 29 Aug 2017
Defense Science Board Terms of Reference - Defense Science Board Task Force on Counter Autonomy, 18 Jun 2018
Demontis et al. Infinity-Norm Support Vector Machines Against Adversarial Label Contamination, 2017
Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J. CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy, 24 Feb 2016
Elsayed, G, Shankar, S., Cheung, B., Papernot, N., Kurakin, A. Goodfellow, I., Sohl-Dickstein, J. Adversarial Examples that Fool both Human and Computer Vision, 22 May 2018
Everitt, T., Krakovna, V., Orseau, L., Hutter, M., and Legg, S Reinforcement Learning with a Corrupted Reward Channel, 19 Aug 2017
Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T., Li, B., Prakash, A., Rahmati, A., and Song, D. Robust Physical-World Attacks on Deep Learning Visual Classification, 27 Jul 2017
Fredrikson, M., Jha, S., Ristenpart, T. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures, 12 Oct 2015
Goodfellow, I., Papernot, N., Huang, S.,Duan, Y., Abbeel, P., Clark, J. Attacking Machine Learning with Adversarial Examples, 24 Feb 2017
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Grosse, K., Papernot, N., Manoharan, P., Backes, M., and McDaniel, P Adversarial Examples for Malware Detection, 12 Aug 2017
Grosse, K., Papernot, N., Manoharan, P., Backes, M., and McDaniel, P Adversarial Perturbations Against Deep Neural Networks for Malware Classification, 16 Jun 2016
Grosse, K., Manoharan, P., Papernot, N., Backes, M., McDaniel, P. On the (Statistical) Detection of Adversarial Examples, 21 Feb 2017
Gu, T., Dolan-Gavitt, B., and Garg, S. BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain, 22 Aug 2017
Hicks, K., Hunter, A.P., Samp, L.S., and Coll, G. Assessing the Third Offset Strategy 2017
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Hosseini, H., Chen, Y., Kannan, S., Zhang, B., Poovendran, R. Blocking Transferability of Adversarial Examples in Black-Box Learning Systems, 13 Mar 2017
Hosseini, H., Xiao, B. and Poovendran, R., Google’s Cloud Vision API Is Not Robust To Noise, 20 Jul 2017
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Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A. Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, 14 Nov 2015
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