Difference between revisions of "Cybersecurity References"
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| + | [https://www.google.com/search?ei=3-M0W6f4C8O45gLx7onoCQ&q=cyber+security+%7Eadversary+artificial+intelligence+attack+threat+%7Edefense&oq=cyber+security+%7Eadversary+artificial+intelligence+attack+threat+%7Edefense Google search...] | ||
| − | + | * [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]] | |
| + | * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]] | ||
| + | * [https://www.csiac.org/resources/cybersecurity-related-websites/ Cybersecurity-related Websites | Cyber Security and Information Systems Information Analysis Center (CSIAC)] | ||
| + | * [https://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research | ||
| + | * [https://whitepapers.virtualprivatelibrary.net/Scholar.pdf Academic and Scholar Search Engines and Sources | Marcus P. Zillman - Virtual Private Library] | ||
| − | + | __________________________________________________________ | |
| − | |||
| − | + | Abadi, M. Chu , A. Goodfellow, I. McMahan, H. Mironov, I. Talwar, K. and Zhang, L. [https://arxiv.org/pdf/1607.00133.pdf Deep Learning with Differential Privacy], 24 Oct 2016 | |
| − | + | Abhijith, Wallace, B., Akhavan-Masouleh, S., Davis, A., Wojnowicz, M., and Brook, J. [https://www.eurotablets.eu/book/1292451282/download-introduction-to-artificial-intelligence-for-security-professionals-the-cylance-press.pdf Introduction to Artificial intelligence for security professionals], 12 Aug 2017 | |
| − | + | Abramson, M. [https://pdfs.semanticscholar.org/b2f7/69ddcf8cae594f39e839aa29b27b98f403ca.pdf Toward Adversarial Online Learning and the Science of Deceptive Machines], 13 Sep 2017 | |
| − | + | Agarap, A. and Pepito, F. [https://arxiv.org/pdf/1801.00318.pdf 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. [https://arxiv.org/pdf/1801.00553.pdf 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. [https://arxiv.org/pdf/1801.02950.pdf Adversarial Deep Learning for Robust Detection of Binary Encoded Malware], 25 Mar 2018 | |
| − | + | Alkasassbeh, M. [https://arxiv.org/ftp/arxiv/papers/1712/1712.09623.pdf An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods], 27 Dec 2017 | |
| − | + | Allen, G., and Chan T. [https://www.belfercenter.org/sites/default/files/files/publication/AI%20NatSec%20-%20final.pdf Artificial Intelligence and National Security - Belfer Center Study], Jul 2017 | |
| − | + | Alzantot, M., Balaji, B., and Srivastava, M. [https://arxiv.org/pdf/1801.00554.pdf Did you hear that? Adversarial Examples Against Automatic] [[Speech Recognition]], 2 Jan 2018 | |
| − | + | Amodei, D. and Olah, C. [https://arxiv.org/pdf/1606.06565.pdf Concrete Problems in AI Safety], 25 Jul 2016 | |
| − | + | Anderson, H.S., Kharkar, A., and Filar, B. [https://www.blackhat.com/docs/us-17/thursday/us-17-Anderson-Bot-Vs-Bot-Evading-Machine-Learning-Malware-Detection-wp.pdf Evading Machine Learning Malware Detection], 27 Jul 2017 | |
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| − | + | Anderson, H.S., Kharkar, A., Filar, B., Evans, D., and Roth, P. [https://arxiv.org/pdf/1801.08917.pdf Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning], 26 Jan 2018 | |
| − | + | Anderson, H.S., Woodbridge, J., and Filar, B. [https://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. [https://arxiv.org/pdf/1708.05866.pdf A Brief Survey of Deep Reinforcement Learning], 28 Sep 2017 | |
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| − | + | Cao, X. and Zhenqiang Gong, N. [https://arxiv.org/pdf/1709.05583.pdf Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification], 11 Jan 2018 | |
| − | + | Carbon Black [https://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. and Wagner, D. [https://arxiv.org/pdf/1705.07263.pdf Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods], 1 Nov 2017 | |
| − | + | Carlini, N. and Wagner, D. [https://arxiv.org/pdf/1801.01944.pdf 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. [https://nicholas.carlini.com/papers/2016_usenix_hiddenvoicecommands.pdf Hidden Voice Commands], 2016 | |
| − | + | Carlini, N. and Wagner, D. [https://arxiv.org/pdf/1711.08478.pdf MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples], 22 Nov 2017 | |
| − | + | Carlini, N. and Wagner, D. [https://arxiv.org/pdf/1608.04644.pdf Towards Evaluating the Robustness of Neural Networks], 22 Mar 2017 | |
| − | + | Chen, H. and Wang FY. [https://www.researchgate.net/publication/242517767_Artificial_Intelligence_for_Homeland_Security Artificial Intelligence for Homeland Security], Jan 2005 | |
| − | + | Chen, P., Sharma, Y., Zhang, H., Yi, J., and Hsieh, C. [https://arxiv.org/pdf/1709.04114.pdf 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. [https://arxiv.org/pdf/1706.04146.pdf 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. [https://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, X., Liu, C., Li, B., Lu, K., and Song, D. [https://arxiv.org/pdf/1712.05526.pdf Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning], 15 Dec 2017 | |
| − | + | Conroy, N. Rubin V. and Chen, Y. [https://www.asist.org/files/meetings/am15/proceedings/submissions/posters/193poster.pdf Automatic Deception Detection: Methods for Finding Fake News], Aug 2017 | |
| − | + | Crawford, K. and Calo, R. [https://www.nature.com/polopoly_fs/1.20805!/menu/main/topColumns/topLeftColumn/pdf/538311a.pdf There is a blind spot in AI research], 20 Oct 2016 | |
| − | + | Das, N., Shanbhogue, M., Chen, S., Chen, L., Kounavis, M., and Chau, D. [https://arxiv.org/pdf/1805.11852.pdf 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. [https://arxiv.org/pdf/1705.02900.pdf 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. [https://arxiv.org/pdf/1802.06816.pdf 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. [https://arxiv.org/pdf/1708.08754.pdf Autoencoder with recurrent neural networks for video forgery detection], 29 Aug 2017 | |
| − | + | Demontis, A., Biggio, B., Fumera, G., Giacintio, G., and Roli, F. [https://ceur-ws.org/Vol-1816/paper-11.pdf Infinity-Norm Support Vector Machines Against Adversarial Label Contamination], 2017 | |
| − | + | Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J. [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/04/CryptonetsTechReport.pdf 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. [https://arxiv.org/pdf/1802.08195.pdf Adversarial Examples that Fool both Human and Computer Vision], 22 May 2018 | |
| − | + | Elsayed, G., Goodfellow, I., and Sohl-Dickstein, J. [https://arxiv.org/pdf/1806.11146.pdf Adversarial Reprogramming of Neural Networks], 28 Jun 2018 | |
| − | + | Everitt, T., Krakovna, V., Orseau, L., Hutter, M., and Legg, S [https://static.ijcai.org/proceedings-2017/0656.pdf 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. [https://arxiv.org/pdf/1707.08945.pdf Robust Physical-World Attacks on Deep Learning Visual Classification], 27 Jul 2017 | |
| − | + | Fawzi, A., Fawzi, H., and Fawzi, O. [https://arxiv.org/pdf/1802.08686.pdf Adversarial vulnerability for any classifier], 23 Feb 2018 | |
| − | + | Folz, J., Palacio, S., Hees, J., Borth, D., and Dengel, A. [https://arxiv.org/pdf/1803.07994.pdf Adversarial Defense based on Structure-to-Signal Autoencoders], 21 Mar 2018 | |
| − | + | Fredrikson, M., Jha, S., and Ristenpart, T. [https://www.cs.cmu.edu/~mfredrik/papers/fjr2015ccs.pdf Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures], 12 Oct 2015 | |
| − | Papernot | + | Goodfellow, I., Papernot, N., Huang, S.,Duan, Y., Abbeel, P., and Clark, J. [https://blog.openai.com/adversarial-example-research/ Attacking Machine Learning with Adversarial Examples], 24 Feb 2017 |
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| − | + | Gopinath, D., Katz, G., Pasareanu, C., and Barrett, C. [https://arxiv.org/pdf/1710.00486.pdf 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. [https://arxiv.org/pdf/1803.00401.pdf Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks], 22 Feb 2018 | |
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| − | + | Grosse, K, Smith, M.T., and Backes, M. [https://arxiv.org/pdf/1806.02032.pdf Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification], 6 Jun 2018 | |
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| − | + | Gu, T., Dolan-Gavitt, B., and Garg, S. [https://machine-learning-and-security.github.io/papers/mlsec17_paper_51.pdf BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain], 22 Aug 2017 | |
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| + | Lu, J., Sibai, H., Fabry, E., and Forsyth, D. [https://arxiv.org/pdf/1707.03501.pdf NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles], 12 jul 2017 | ||
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