Cybersecurity References
- Cybersecurity
- Government Services for other related papers
__________________________________________________________
Akhtar, N. and 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., 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
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
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
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
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. 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. 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
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
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., 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
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., Xiao, B. and Poovendran, R., Google’s Cloud Vision API Is Not Robust To Noise, 20 Jul 2017
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
Lin, Y., Liu, M., Sun, M., and Huang, J. Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight, 2 Oct 2017
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
Mayer, M. Norwegian Institute for Defence Studies, Oslo IFS Insights, Apr 2018
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
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
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
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., 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
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
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
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
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
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: 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 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. 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 C. Evolutionary Generative Adversarial Networks, 1 Mar 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
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 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
