Cybersecurity References
______________________________________________________
Arulkumaran, K., Deisenroth, M., Brundage, M., and Bharath, A. A Brief Survey of Deep Reinforcement Learning, 28 Sep 2017
Akhtar, N., Mian, A. 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
______________________________________________________
Abadi, M. Chu , A. Goodfellow, I. McMahan, H. Mironov, I. Talwar, K. and Zhang, L. Deep Learning with Differential Privacy, 24 Oct 2016
Abhijith, Wallace, B., Akhavan-Masouleh, S., Davis, A., Wojnowicz, M., Brook, J. 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
Allen, G., Chan T. Artificial Intelligence and National Security - BELFER CENTER STUDY, Jul 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
Anderson, H.S., Kharkar, A., Filar, B., Evans, D., and Roth, P. Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning, 26 Jan 2018
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 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
Chen, H., Wang FY. Artificial Intelligence for Homeland Security, Jan 2005
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
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
Goodfellow, I., Shlens, J., Szegedy C. Explaining and Harnessing Adversarial Examples, 20 Mar 2015
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y. Generative Adversarial Nets, 10 Jun 2014
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
Hitawala, S. Comparative Study on Generative Adversarial Networks, 12 Jan 2018
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
Hu, W., Tan, Y. Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN, ..MalGAN 20 Feb 2017
Huang, L., Joseph, A., Neson, B., Rubinstein, B., Tygar, J.D. Adversarial Machine Learning, Oct 2011
Jin J., Dundar, A., Culurciello, E. Robust Convolutional Neural Networks under Adversarial Noise, 25 Feb 2016
Kantarcioglu, M., Xi, B. Adversarial Data Mining for Cyber Security, 28 Oct 2016
Kantchelian, A., Tygar, J.D., Joseph, A. Evasion and Hardening of Tree Ensemble Classifiers, 27 May 2016
Kantchelian, A. Taming Evasions in Machine Learning Based Detection, 12 Aug 2016
Kashyap, A., Parmar, R., Agarwal, M., Gupta, H. An Evaluation of Digital Image Forgery Detection Approaches, 30 Mar 2017
Kolosnjaji, B., Demontiz, A., Biggio, B., Maiorca, D., Giacinto, G., Eckert, C., Roli, F. Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables, 12 Mar 2018
Kreuk, F., Barak, A., Aviv-Reuven, S., Baruch, M., Pinkas, B., Keshet, J. Adversarial Examples on Discrete Sequences for Beating Whole-Binary Malware Detection, 13 Feb 2018
Kreuk, F., Barak, A., Aviv-Reuven, S., Baruch, M., Pinkas, B., Keshet, J. Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples, 13 May 2018
Kurakin, A., Goodfellow, I., Bengio, S. Adversarial examples in the physical world, 11 Feb 2017
Laskov, P., Lippmann, R. Machine Learning in Adversarial Environments, 28 Jun 2010
Lewis, L. Insights for the Third Offset: Addressing Challenges of Autonomy and Artificial Intelligence in Military Operations, Sep 2017
Lu, P., Chen, P., Chen, K., Yu, C. On the Limitation of MagNet Defense against L1-based Adversarial Examples, 9 May 2018
Luo, B., Liu, Y. Wei, L., Xu, Q. Towards Imperceptible and Robust Adversarial Example Attacks against Neural, 15 Jan 2018
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A. Towards Deep Learning Models Resistant to Adversarial Attacks, 19 Jun 2017
Mayer, M. Norwegian Institute for Defence Studies, Oslo IFS Insights, Apr 2018
Meng, D., Chen, H. MagNet: a Two-Pronged Defense against Adversarial Examples, 11 Sep 2017
Miller, D., Hu, X., Qiu, Z., Kesidis, G. Adversarial Learning. A Critical Review and Active Learning Study, 27 May 2017
Muñoz-González, L, Bissio, B., Demontis, A., Paudice, A., Wongreassamee, V., Lupu, E., Roli, F. Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization, 29 Aug 2017
Nataraj, L A Signal Processing Approach To Malware Analysis, Dec 2015
Naveiro, R., Redondo, A., Insua, D., Ruggeri, F. Adversarial classification: An adversarial risk analysis approach, 21 Feb 2018
Nguyen A, Yosinski J, Clune J. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, 2 Apr 2015 Video
North Atlantic Treaty Organization: Joint Air Power Competence Centre NATO Joint Air Power and Offensive Cyber Operations, Nov 2017
Norton, A, Qi, Y. Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning, 1 Aug 2017
Ororbia II, A., Giles, C., Kifer, D. Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization, 29 Jul 2016
Papernot N., Goodfellow, I., Erlingsson, U., McDaniel, P. Adversarial Examples in Machine Learning, 1 Feb 2017
Papernot N., Goodfellow, I., Sheatsley, R., Feinman, R., McDaniel, P. Cleverhans v.1.0.0: an adversarial machine learning library, 14 Dec 2016
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A. Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, 14 Nov 2015
Papernot, N., McDaniel, P, Jha, S., Fredrikson, M., Celik, Z.B., Swami, A. The Limitations of Deep Learning in Adversarial Settings, 24 Nov 2015
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B. and Swami, A Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples, 19 Feb 2016
Papernot et al. Practical Black-Box Attacks against Machine Learning, 8 Feb 2016
Papernot, N., McDaniel, P., Sinha, A., and Wellman, Towards the Science of Security and Privacy in Machine Learning, 11 Nov 2016
Papernot, N., McDaniel, P., Goodfellow I. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples, 24 May 2016
Paudice et al. Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection, 8 Feb 2018
Radford, A., Metz, L. and Chintala, S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 7 Jan 2016
Raghunathan, A., Steinhardt, J., Liang, P. Certified Defenses against Adversarial Examples, 29 Jan 2018
Rahman, M., Azimpourkivi, M., Topkara, U., Carbunar, B. Video Liveness for Citizen Journalism: Attacks and Defenses, Apr 2017
Rouhani, B., Riazi, M., and Koushanfar, F. CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning, 1 Apr 2018
Rouhani, B., Riazi, M., and Koushanfar, F. DeepSecure: Scalable Provably-Secure Deep Learning, 24 May 2017
Rubinstein, B., Nelson, B., Huang, L., Joseph, A., Lau, S., Rao, S., Taft, N., and Tygar, J.D. ANTIDOTE: Understanding and Defending against Poisoning of Anomaly Detectors, 2009
Schneier, B. The Internet of Things is Wildly Insecure--and Often Unpatchable, 2014
Schneier, B. Security and the Internet of Things, 2017
Shen, S., Tople, S., Saxena, P. AUROR: Defending Against Poisoning Attacks in Collaborative Deep Learning Systems, 5 Dec 2016
Shokri, R., Stronati, M., and Shmatikov, V. Membership Inference Attacks Against Machine Learning Models, 31 Mar 2017
Šrndic, N. and Laskov, P. Practical Evasion of a Learning-Based Classifier: A Case Study, 2014
Stevens, R., Suciu, O., Ruef, A., Hong, S., Hicks, M., Dumitras, T. Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning, 17 Jan 2017
Stokes, J., Wang, D., Marinescu, M., Mariono, M., Bussone, B. Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models 16 Dec 2017
Stoica, I., Song, D., Popa, R., Patterson, D., Mahoney, M., Katz, R., Joseph, A., Jordan, M., Hellerstein, J., Gonzalez, J., Goldberg, K., Ghodsi, A., Culler, D., and Abbeel, P. A Berkeley View of Systems Challenges for AI, 15 Dec 2017
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. and Fergus, R. Intriguing properties of neural networks, 19 Feb 2014
Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., McDaniel, P. The Space of Transferable Adversarial Examples, 23 May 2017
Uesato, J., O'Donoghue, B., Oord, A., Kohli, P. Adversarial Risk and the Dangers of Evaluating Against Weak Attacks, 12 Jun 2018
U.S. Department of Defense Law of War Manual, Chapter XVI | Cyber Operations, 2015
U.S. Department of Defense: US Air Force Artificial Intelligence and National Security, 26 Apr 2018
U.S. Department of Defense: U.S. Defense Science Board Report of the Defense Science Board (DSB) Task Force on Cyber Supply Chain, Apr 2017
U.S. Department of Defense: U.S. Defense Science Board Terms of Reference | Defense Science Board (DSB) Task Force on Counter Autonomy, 18 Jun 2018
U.S. Government: American Technology Council (ATC) Report to the President on IT Modernization, 2017
U.S. Government: White House 2018 White House Summit on Artificial Intelligence for American Industry, 10 May 2018
U.S. Department of Homeland Security Artificial Intelligence White Paper | Science and Technology Advisory Committee (HSSTAC): Quadrennial Homeland Security Review Subcommittee, 10 Mar 2017
U.S. Department of Homeland Security Narrative Analysis: Artificial Intelligence | National Protection and Programs Directorate - Office of Cyber and Infrastructure Analysis, July 2017
Uther, W., Veloso, M. Adversarial Reinforcement Learning, Jan 2003
Waltzmann, R. The Weaponization of Information: The Need for Cognitive Security, testimony presented before the Senate Armed Services Committee, Subcommittee on Cybersecurity, 27 Apr 2017
Wang, Q. Guo, W., Zhang, K., Ororbia II, A., Xing, X., Giles, C., Liu, X. Adversary Resistant Deep Neural Networks with an Application to Malware Detection, 27 Apr 2017
Wang C. Evolutionary Generative Adversarial Networks, 1 Mar 2018
Xiao, H. Adversarial and Secure Machine Learning, 27 Oct 2016
Xu, W., Qi, Y., Evans, D. Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers, Feb 2016
Xu, W., Evans, D., Qi, Y. Feature Squeezing; Detecting Adversarial Examples in Deep Neural Networks, 5 Dec 2017
Yampolskiy, R., Spellchecker, M.S. Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures, Oct 2016
Yan J., Qi, Y., Rao, Q Detecting Malware with an Ensemble Method Based on Deep Neural Network, 18 Aug 2017
Yuan, X., He, P., Zhu, Q., Bhat, R., Li, X. Adversarial Examples. Attacks and Defenses for Deep Learning, 5 2018]
Zane, C., Markel, A. Machine Learning Malware Detection, 2015
Zhang, C., Bengio S., Hardt, M., Recht, B., Vinyals, O. Understanding deep learning requires rethinking generalization, 26 Feb 2017
