Cybersecurity References

Jump to: navigation, search

Google search...


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., and Brook, J. Introduction to Artificial intelligence for security professionals, 12 Aug 2017

Abramson, M. Toward Adversarial Online Learning and the Science of Deceptive Machines, 13 Sep 2017

Agarap, A. and Pepito, F. Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification, 31 Dec 2017

Akhtar, N. and Mian, A. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey, 26 Feb 2018

Al-Dujaili, A., Haung, A., Hemberg, E., and O'Reilly, U. Adversarial Deep Learning for Robust Detection of Binary Encoded Malware, 25 Mar 2018

Alkasassbeh, M. An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods, 27 Dec 2017

Allen, G., and Chan T. Artificial Intelligence and National Security - Belfer Center Study, Jul 2017

Alzantot, M., Balaji, B., and Srivastava, M. Did you hear that? Adversarial Examples Against Automatic Speech Recognition, 2 Jan 2018

Amodei, D. and Olah, C. Concrete Problems in AI Safety, 25 Jul 2016

Anderson, H.S., Kharkar, A., and 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

Arulkumaran, K., Deisenroth, M., Brundage, M., and Bharath, A. A Brief Survey of Deep Reinforcement Learning, 28 Sep 2017

Athalye, A. and Carlini, N. On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses, 10 Apr 2018

Bao, R., Liang, S., and Wang, Q. Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference, 21 May 2018

Barreno, M., Nelson, B., Sears, R., Joseph, A., and Tygar, J.D. Can Machine Learning Be Secure?, 21 Mar 2016

Bastani, O., Kim, C., and Bastani, H. Interpreting Blackbox Models via Model Extraction, 22 May 2018

Bauer, H., Burkacky, O., and Knochenhauer, C. [1], May 2017

Biggio, B., Nelson, B., and Laskov, P. Poisoning Attacks against Support Vector Machines, 25 Mar 2013

Biggio, B., Nelson, B., and Laskov, P. Support Vector Machines Under Adversarial Label Noise, 2011

Brundage et al. The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, Feb 2018

Bulò, S., Biggio, B., Pillai, I., Pellillo, M., and Roli, F. Randomized Prediction Games for Adversarial Machine Learning, 11 Nov 2017

Cao, X. and Zhenqiang Gong, N. Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification, 11 Jan 2018

Carbon Black Beyond the Hype: Security Experts Weigh in on Artificial Intelligence, Machine Learning, and Non-Malware Attacks, 2017

Carlini, N. and Wagner, D. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods, 1 Nov 2017

Carlini, N. and 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. and Wagner, D. MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples, 22 Nov 2017

Carlini, N. and Wagner, D. Towards Evaluating the Robustness of Neural Networks, 22 Mar 2017

Chen, H. and Wang FY. Artificial Intelligence for Homeland Security, Jan 2005

Chen, P., Sharma, Y., Zhang, H., Yi, J., and 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., and Li, B. Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach, 31 Oct 2017

Chen, S., Xue, M., Fan, L., and 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., and Song, D. Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning, 15 Dec 2017

Conroy, N. Rubin V. and 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

Das, N., Shanbhogue, M., Chen, S., Chen, L., Kounavis, M., and Chau, D. ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio, 30 May 2018

Das, N., Shanbhogue, M., Chen, S., Hohman, F., Chen, L., Kounavis, M., and Chau, D. Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression, 8 May 2017

Das, N., Shanbhogue, M., Chen, S., Hohman, F., Li, S., Chen, L., Kounavis, M., and Chau, D. Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression, 19 Feb 2018

D’Avino, D., Cozzolino, D., Poggi, G., and Verdoliva, L. Autoencoder with recurrent neural networks for video forgery detection, 29 Aug 2017

Demontis, A., Biggio, B., Fumera, G., Giacintio, G., and Roli, F. 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., and Sohl-Dickstein, J. Adversarial Examples that Fool both Human and Computer Vision, 22 May 2018

Elsayed, G., Goodfellow, I., and Sohl-Dickstein, J. Adversarial Reprogramming of Neural Networks, 28 Jun 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

Fawzi, A., Fawzi, H., and Fawzi, O. Adversarial vulnerability for any classifier, 23 Feb 2018

Folz, J., Palacio, S., Hees, J., Borth, D., and Dengel, A. Adversarial Defense based on Structure-to-Signal Autoencoders, 21 Mar 2018

Fredrikson, M., Jha, S., and 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., and Clark, J. Attacking Machine Learning with Adversarial Examples, 24 Feb 2017

Goodfellow, I., Shlens, J., and 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., and Bengio, Y. Generative Adversarial Nets, 10 Jun 2014

Gopinath, D., Katz, G., Pasareanu, C., and Barrett, C. DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks, 2 Oct 2017

Goswami, G., Ratha, N., Agarwal, A., Singh, R., and Vatsa, M. Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks, 22 Feb 2018

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, Pfaff, D., Smith, M.T., and Backes, M. How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models, 16 Feb 2018

Grosse, K, Smith, M.T., and Backes, M. Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification, 6 Jun 2018

Grosse, K., Manoharan, P., Papernot, N., Backes, M., and 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

Hendrycks, D. and Dietterich, T. Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations, 4 Jul 2018

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

Homoliak, I., Toffalini, F., Guarnizo, J., Elovici, Y., and Ochoa, M. Insight into Insiders: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures, 4 May 2018

Hosseini, H., Chen, Y., Kannan, S., Zhang, B., and Poovendran, R. Blocking Transferability of Adversarial Examples in Black-Box Learning Systems, 13 Mar 2017

Hosseini, H. and Poovendran, R. Semantic Adversarial Examples, 16 Mar 2018

Hosseini, H., Xiao, B. and Poovendran, R. Google’s Cloud Vision API Is Not Robust To Noise, 20 Jul 2017

Jakubovitz, D. and Giryes, R. Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization, 23 Mar 2018

Lu, P., Chen, P., and Yu, C. On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples, 26 Mar 2018

Hu, W. and 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., and Tygar, J.D. Adversarial Machine Learning, Oct 2011

Ilyas, A., Engstrom, L., Athalye, A., and Lin, J. Black-box Adversarial Attacks with Limited Queries and Information, 7 Jun 2018

Ilyas, A., Jalal, A., Asteri, E., Daskalakis, C., and Dimakis, A.G. The Robust Manifold Defense: Adversarial Training using Generative Models, 26 Dec 2017

Jia, J. and Gong, N. AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning, 13 May 2018

Jin J., Dundar, A., and Culurciello, E. Robust Convolutional Neural Networks under Adversarial Noise, 25 Feb 2016

Kantarcioglu, M. and Xi, B. Adversarial Data Mining for Cyber Security, 28 Oct 2016

Kantchelian, A., Tygar, J.D., and 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., and 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., and Roli, F. Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables, 12 Mar 2018

Kouzemtchenko, A. Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions, 23 Jun 2018

Kreuk, F., Barak, A., Aviv-Reuven, S., Baruch, M., Pinkas, B., and 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., and Keshet, J. Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples, 13 May 2018

Kurakin, A., Goodfellow, I., and Bengio, S. Adversarial examples in the physical world, 11 Feb 2017

Kurakin, A., Goodfellow, I., Bengio, S., Dong, Y., Liao, F., Liang, M., Pang, T., Zhu, J., Hu, X., Xie, C., Wang, J., Zhang, Z., Ren, Z., Yuille, A., Huang, S., Zhao, Y., Zhao, Y., Han, Z., Long, J., Berdibekov, Y., Akiba, T., Tokui, S., and Abe, M. Adversarial Attacks and Defences Competition - Google Brain organized a NIPS 2017 competition, 31 Mar 2018

Laskov, P. and Lippmann, R. Machine Learning in Adversarial Environments, 28 Jun 2010

Lassance, C., Gripon, V., and Ortega, A. Laplacian Power Networks: Bounding Indicator Function Smoothness for Adversarial Defense, 24 May 2018

Lewis, L. Insights for the Third Offset: Addressing Challenges of Autonomy and Artificial Intelligence in Military Operations, Sep 2017

Li, Y. Generative Classifiers More Robust to Adversarial Attacks?, 19 Feb 2018

Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., and Zhu, J. Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser, 8 May 2018

Lin, Y., Liu, M., Sun, M., and Huang, J. Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight, 2 Oct 2017

Liu, Z., Liu, Q., Liu, T., Wang, Y., and Wen, W. Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples, 14 Mar 2018

Liu, Y., Chen, J., and Chen, H. Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks, 9 Jan 2018

Liu, Q., Liu, T., Liu, Z., Wang, Y., Jin, Y., and Wen, W. Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks, 19 Mar 2018

Lu, J., Sibai, H., Fabry, E., and Forsyth, D. NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles, 12 jul 2017

Lu, P., Chen, P., Chen, K., and Yu, C. On the Limitation of MagNet Defense against L1-based Adversarial Examples, 9 May 2018

Luo, B., Liu, Y. Wei, L., and Xu, Q. Towards Imperceptible and Robust Adversarial Example Attacks against Neural, 15 Jan 2018

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. Towards Deep Learning Models Resistant to Adversarial Attacks, 19 Jun 2017

Maiorca, D., Biggio, B., Chiappe, M., and Giacinto, G. Adversarial Detection of Flash Malware: Limitations and Open Issues, 27 Oct 2017

Mayer, M. Norwegian Institute for Defence Studies, Oslo IFS Insights, Apr 2018

Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N., Guarnizo, J., and Elovici, Y. Detection of Unauthorized IoT Devices Using Machine Learning Technique, 14 Sep 2017

Meng, D. and Chen, H. MagNet: a Two-Pronged Defense against Adversarial Examples, 11 Sep 2017

Miller, D., Hu, X., Qiu, Z., and Kesidis, G. Adversarial Learning. A Critical Review and Active Learning Study, 27 May 2017

Moosavi-Dezfooli, S., Shrivastava, A., and Tuzel, O. Divide, Denoise, and Defend against Adversarial Attacks, 19 Feb 2018

Muñoz-González, L, Bissio, B., Demontis, A., Paudice, A., Wongreassamee, V., Lupu, E., and Roli, F. Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization, 29 Aug 2017

Naseer, M., Khan, S., Porikli, F. Local Gradients Smoothing: Defense against localized adversarial attacks, 2 Jul 2018

Nataraj, L A Signal Processing Approach To Malware Analysis, Dec 2015

Naveiro, R., Redondo, A., Insua, D., and Ruggeri, F. Adversarial classification: An adversarial risk analysis approach, 21 Feb 2018

Nguyen A, Yosinski J, and Clune J. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, 2 Apr 2015 Video

Nayebi, A. and Ganguli, S. Biologically inspired protection of deep networks from adversarial attacks, 27 Mar 2017

Nicolae, M., Sinn, M., Tran, M., Rawat, A., Wistuba, M., Zantedeschi, V., Molloy, I., Edwards, B. Adversarial Robustness Toolbox v0.2.2, 3 Jul 2018

North Atlantic Treaty Organization (NATO): Cooperative Cyber Defence Centre of Excellence; Minárik, T., Jakschis, R., and Lindström, L. 10th International Conference on Cyber Conflict CyCon X: Maximising Effects, 30 May 2018

North Atlantic Treaty Organization (NATO): Joint Air Power Competence Centre NATO Joint Air Power and Offensive Cyber Operations, Nov 2017

North Atlantic Treaty Organization (NATO): U.S. Department of Defense: U.S. Army Research Laboratory Initial Reference Architecture of an Intelligent Autonomous Agent for Cyber Defense, Mar 2018

North Atlantic Treaty Organization (NATO): U.S. Department of Defense: U.S. Army Research Laboratory - Research Group IST-152-RTG Toward Intelligent Autonomous Agents for Cyber Defense: Report of the 2017 Workshop, Apr 2018

Norton, A. and Qi, Y. Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning, 1 Aug 2017

Ororbia II, A., Giles, C., and Kifer, D. Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization, 29 Jul 2016

Papernot N., Goodfellow, I., Erlingsson, U., and McDaniel, P. Adversarial Examples in Machine Learning, 1 Feb 2017

Papernot N., Goodfellow, I., Sheatsley, R., Feinman, R., and McDaniel, P. Cleverhans v.1.0.0: an adversarial machine learning library, 14 Dec 2016

Papernot, N., McDaniel, P., Wu, X., Jha, S., and 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., and 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, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B. and Swami, A. Practical Black-Box Attacks against Machine Learning, 8 Feb 2016

Papernot, N., McDaniel, P., Sinha, A., and Wellman M. Towards the Science of Security and Privacy in Machine Learning, 11 Nov 2016

Papernot, N. et al. Technical Report on the CleverHans v2.1.0 Adversarial Examples Library, 27 Jun 2018

Papernot, N., McDaniel, P., and Goodfellow I. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples, 24 May 2016

Paudice A., Muñoz-González, L., Gyorgy, A., and Lupu, E. Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection, 8 Feb 2018

Prakash, A., Moran, N., Garber, S., DiLillo, A., and Storer, J. Deflecting Adversarial Attacks with Pixel Deflection, 30 Mar 2018

Radford, A., Metz, L. and Chintala, S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 7 Jan 2016

Raghunathan, A., Steinhardt, J., and Liang, P. Certified Defenses against Adversarial Examples, 29 Jan 2018

Rahman, M., Azimpourkivi, M., Topkara, U., and Carbunar, B. Video Liveness for Citizen Journalism: Attacks and Defenses, Apr 2017

Ranjan, R., Sankaranarayanan, S., Castillo, Carlos D., and Chellappa, R. Improving Network Robustness against Adversarial Attacks with Compact Convolution, 22 Mar 2018

Ross, Andrew and Doshi-Velez, F. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients, 26 Nov 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

Samangouei, P., Kabkab, M., and Chellappa, R. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models, 18 May 2018

Santhanam, G. and Grnarova P. Defending Against Adversarial Attacks by Leveraging an Entire GAN, audio, 27 May 2018

Schneier, B. The Internet of Things is Wildly Insecure--and Often Unpatchable, 2014

Schneier, B. Security and the Internet of Things, 2017

Shafahi, A., Ronny H.W., Najibi, M., Suciu, O., Studer, C., Dumitras, T., and Goldstein, T. Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks, 3 Apr 2018

Sharif, M., Bauer, L., and Reiter, M. On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples, 27 Feb 2018

Shen, S., Tople, S., and 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

Singh T. and Kantardzic, M. Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains, 23 Mar 2017

Sitawarin, C., Bhagoji, A., Mosenia, A., Chiang, M., and Mittal P. DARTS: Deceiving Autonomous Cars with Toxic Signs, 31 May 2018

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., and Dumitras, T. Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning, 17 Jan 2017

Stokes, J., Wang, D., Marinescu, M., Mariono, M., and 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

Sun, Z., Ozay, M., and Okatani, T. HyperNetworks with statistical filtering for defending adversarial examples, 6 Nov 2017

Svoboda, J., Masci, J., Monti, F., Bronstein, M., and Guibas, L. PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks, 31 May 2018

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., and McDaniel, P. The Space of Transferable Adversarial Examples, 23 May 2017

Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P. Ensemble Adversarial Training: Attacks and Defenses, 30 Jan 2018

Tretschk, E., Oh, S., and Fritz, M. Sequential Attacks on Agents for Long-Term Adversarial Goals, 5 Jul 2018

Tsuzuku, Y., Sato, I., Sugiyama, M. Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks, 22 May 2018

Uesato, J., O'Donoghue, B., Oord, A., and 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. Army Cyber Institute at West Point and Arizona State University The New Dogs of War: The Future of Weaponized Artificial Intelligence, 2017

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 (DHS) Artificial Intelligence White Paper | Science and Technology Advisory Committee (HSSTAC): Quadrennial Homeland Security Review Subcommittee, 10 Mar 2017

U.S. Department of Homeland Security (DHS) Narrative Analysis: Artificial Intelligence | National Protection and Programs Directorate - Office of Cyber and Infrastructure Analysis, July 2017

Uther, W. and Veloso, M. Adversarial Reinforcement Learning, Jan 2003

Vijaykeerthy, D., Suri, A., Mehta, S., and Kumaraguru, P. Hardening Deep Neural Networks via Adversarial Model Cascades, 12 Feb 2018

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., and Liu, X. Adversary Resistant Deep Neural Networks with an Application to Malware Detection, 27 Apr 2017

Wang, D., Li, C., Wen, S., Nepal, S., and Xiang, Y. Defensive Collaborative Multi-task Training - Defending against Adversarial Attack towards Deep Neural Networks, 3 Jul 2018

Wang C. Evolutionary Generative Adversarial Networks, 1 Mar 2018

Warde-Farley, D. and Goodfellow, I. Adversarial Perturbations of Deep Neural Networks, 23 Dec 2016

Weng, T., Zhang, H., Chen, P., Yi, J., Su, D., Gao, Y., Hsieh, C., and Daniel, L. Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach, 31 Jan 2018

Xiao, H. Adversarial and Secure Machine Learning, 27 Oct 2016

Xie, C., Zhang, Z., Wang, J., Zhou, Y., Ren, Z., Yuille, A. Improving Transferability of Adversarial Examples with Input Diversity, 11 Jun 2018

Xie, C., Zhang, Z., Wang, J., Zhou, Y., Ren, Z., Yuille, A. Mitigating Adversarial Effects Through Randomization 28 Feb 2018

Xu, W., Qi, Y., and Evans, D. Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers, Feb 2016

Xu, W., Evans, D., and Qi, Y. Feature Squeezing; Detecting Adversarial Examples in Deep Neural Networks, 5 Dec 2017

Yampolskiy, R., and Spellchecker, M.S. Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures, Oct 2016

Yan, Z., Guo, Y., and Zhang, C. Deep Defense: Training DNNs with Improved Adversarial Robustness, 30 May 2018

Yan J., Qi, Y., and Rao, Q Detecting Malware with an Ensemble Method Based on Deep Neural Network, 18 Aug 2017

Yuan, X., He, P., Zhu, Q., Bhat, R., and Li, X. Adversarial Examples. Attacks and Defenses for Deep Learning, 5 Jan 2018

Zane, C. and Markel, A. Machine Learning Malware Detection, 2015

Zhang, C., Bengio S., Hardt, M., Recht, B., and Vinyals, O. Understanding deep learning requires rethinking generalization, 26 Feb 2017

Zhao, P., Fu, Z., Wu, O., Hu, Q., and Wang, J. Detecting Adversarial Examples via Key-based Network, 2 Jun 2018