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In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security.
Defense Methods Against Adversarial Examples for Recurrent Neural Networks
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ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
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Allergy attack against automatic signature generation . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4219 LNCS
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Sponge Examples: Energy-Latency Attacks on Neural Networks
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IEMOCAP: interactive emotional dyadic motion capture database
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Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15-19, 2008, Pisa, Italy . IEEE Computer Society, 413–422
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Exploiting Machine Learning to Subvert Your Spam Filter. In First USENIX Workshop on Large-Scale Exploits and Emergent Threats, LEET ’08, San Francisco, CA, USA, April 15, 2008, Proceedings , Fabian Monrose (Ed.). USENIX Association
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Effective and Efficient Malware Detection at the End Host. In Proceedings of the 18th Conference on USENIX Security Symposium (Montreal, Canada) (SSYM’09) . USENIX Association, Berkeley, CA, USA, 351–366
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A Detailed Analysis of the KDD CUP 99 Data Set. In Proceedings of the Second IEEE International Conference on Computational Intelligence for Security and Defense Applications (Ottawa, Ontario, Canada) (CISDA’09) . IEEE Press, Piscataway, NJ, USA, 53–58
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The security of machine learning
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Automatic analysis of malware behavior using machine learning
Konrad Rieck, Philipp Trinius, Carsten Willems, and Thorsten Holz. 2011 · 2010
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Detecting Algorithmically Generated Malicious Domain Names. In Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement (Melbourne, Australia) (IMC ’10) . ACM, New York, NY, USA, 48–61
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Adversarial machine learning. In Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, AISec 2011, Chicago, IL, USA, October 21, 2011 , Yan Chen, Alvaro A. Cárdenas, Rachel Greenstadt, and Benjamin I. P. Rubinstein (Eds.). ACM, 43–58
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From Throw-Away Traffic to Bots: Detecting the Rise of DGA-Based Malware. In Presented as part of the 21st USENIX Security Symposium (USENIX Security 12) . USENIX, Bellevue, WA, 491–506
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Static Prediction Games for Adversarial Learning Problems
Michael Brückner, Christian Kanzow, and Tobias Scheffer. 2012 · 2012
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Detecting Algorithmically Generated Domain-Flux Attacks With DNS Traffic Analysis
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Evasion Attacks against Machine Learning at Test Time
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Security Evaluation of Pattern Classifiers under Attack
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DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket. In 21st Annual Network and Distributed System Security Symposium, NDSS 2014, San Diego, California, USA, February 23-26, 2014 . The Internet Society
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On the Properties of Neural Machine Translation: Encoder–Decoder Approaches. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation . Association for Computational Linguistics
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
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Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada , Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 2672–2680
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014 · 2014
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Phoenix: DGA-Based Botnet Tracking and Intelligence. In Detection of Intrusions and Malware, and Vulnerability Assessment , Sven Dietrich (Ed.). Springer International Publishing, Cham, 192–211
Stefano Schiavoni, Federico Maggi, Lorenzo Cavallaro, and Stefano Zanero. 2014 · 2014
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Practical Evasion of a Learning-Based Classifier: A Case Study. In 2014 IEEE Symposium on Security and Privacy, SP 2014, Berkeley, CA, USA, May 18-21, 2014 . IEEE Computer Society, 197–211
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Deep Learning Face Representation from Predicting 10,000 Classes. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’14) . IEEE Computer Society, Washington, DC, USA, 1891–1898
Yi Sun, Xiaogang Wang, and Xiaoou Tang. 2014 · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014 · 2014
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Explaining and Harnessing Adversarial Examples
I. J. Goodfellow, J. Shlens, and C. Szegedy. 2015 · 2015
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Replacement Attacks: Automatically Impeding Behavior-Based Malware Specifications. In Applied Cryptography and Network Security - 13th International Conference, ACNS 2015, New York, NY, USA, June 2-5, 2015, Revised Selected Papers (Lecture Notes in Computer Science) , Tal Malkin, Vladimir Kolesnikov, Allison Bishop Lewko, and Michalis Polychronakis (Eds.), Vol. 9092. Springer, 497–517
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Deep Face Recognition. In British Machine Vision Conference
O. M. Parkhi, A. Vedaldi, and A. Zisserman. 2015 · 2015
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Deep neural network based malware detection using two dimensional binary program features. In 2015 10th International Conference on Malicious and Unwanted Software (MALWARE) . IEEE
Joshua Saxe and Konstantin Berlin. 2015 · 2015
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On the Character of Phishing URLs: Accurate and Robust Statistical Learning Classifiers. In Proceedings of the 5th ACM Conference on Data and Application Security and Privacy (San Antonio, Texas, USA) (CODASPY ’15) . ACM, New York, NY, USA, 111–122
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DeepDGA: Adversarially-Tuned Domain Generation and Detection. In Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, AISec@CCS 2016, Vienna, Austria, October 28, 2016 , David Mandell Freeman, Aikaterini Mitrokotsa, and Arunesh Sinha (Eds.). ACM, 13–21
Hyrum S. Anderson, Jonathan Woodbridge, and Bobby Filar. 2016 · 2016
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Adversarial Perturbations Against Deep Neural Networks for Malware Classification
K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. McDaniel. 2016 · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2016 · 2016
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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . IEEE Computer Society, 2574–2582
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016 · 2016
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The Limitations of Deep Learning in Adversarial Settings. In 2016 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. 2016b · 2016
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Crafting adversarial input sequences for recurrent neural networks. In MILCOM 2016 - 2016 IEEE Military Communications Conference . IEEE
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang. 2016c · 2016
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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick D. McDaniel, and Ian J. Goodfellow. 2016a · 2016
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Alec Radford, Luke Metz, and Soumith Chintala. 2016 · 2016
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Bypassing system calls-based intrusion detection systems
Ishai Rosenberg and Ehud Gudes. 2016 · 2016
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Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (Vienna, Austria) (CCS ’16) . ACM, New York, NY, USA, 1528–1540
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter. 2016 · 2016
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Stealing Machine Learning Models via Prediction APIs. In 25th USENIX Security Symposium, USENIX Security 16, Austin, TX, USA, August 10-12, 2016. , Thorsten Holz and Stefan Savage (Eds.). USENIX Association, 601–618
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. 2016 · 2016
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Evading Machine Learning Malware Detection. In Black Hat US
Hyrum S. Anderson, Anant Kharkar, Bobby Filar, and Phil Roth. 2017 · 2017
Cited alongside, same era.
Classifying phishing URLs using recurrent neural networks. In 2017 APWG Symposium on Electronic Crime Research (eCrime) . 1–8
A. C. Bahnsen, E. C. Bohorquez, S. Villegas, J. Vargas, and F. A. González. 2017 · 2017
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Adversarial Examples Are Not Easily Detected. In Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security - AISec 2017 . ACM Press
Nicholas Carlini and David Wagner. 2017a · 2017
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Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, CA, USA, May 22-26, 2017 . IEEE Computer Society, 39–57
Nicholas Carlini and David A. Wagner. 2017b · 2017
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Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach
Adversarial Examples for Natural Language Classification Problems
Volodymyr Kuleshov, Shantanu Thakoor, Tingfung Lau, and Stefano Ermon. 2018 · 2018
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Discrete Attacks and Submodular Optimization with Applications to Text Classification
Qi Lei, Lingfei Wu, Pin-Yu Chen, Alexandros G. Dimakis, Inderjit S. Dhillon, and Michael Witbrock. 2018 · 2018
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IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection
Zilong Lin, Yong Shi, and Zhi Xue. 2018 · 2018
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Trojaning Attack on Neural Networks. In 25nd Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February 18-221, 2018 . The Internet Society
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Sen Chen, Minhui Xue, Lingling Fan, Shuang Hao, Lihua Xu, Haojin Zhu, and Bo Li. 2018b · 2017
Cited alongside, same era.
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. 2017a · 2017
Cited alongside, same era.
Evading Classifiers by Morphing in the Dark. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017 , Bhavani M. Thuraisingham, David Evans, Tal Malkin, and Dongyan Xu (Eds.). ACM, 119–133
Hung Dang, Yue Huang, and Ee-Chien Chang. 2017 · 2017
Cited alongside, same era.
A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems
Cheng Feng, Tingting Li, Zhanxing Zhu, and Deeph Chana. 2017 · 2017
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Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey
Seyed Mohammad Ghaffarian and Hamid Reza Shahriari. 2017 · 2017
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Crafting Adversarial Examples For Speech Paralinguistics Applications
Yuan Gong and Christian Poellabauer. 2017 · 2017
Cited alongside, same era.
Adversarial Examples for Malware Detection
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel. 2017 · 2017
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Adversarial Example Defense: Ensembles of Weak Defenses are not Strong. In 11th USENIX Workshop on Offensive Technologies, WOOT 2017, Vancouver, BC, Canada, August 14-15, 2017. , William Enck and Collin Mulliner (Eds.). USENIX Association
Warren He, James Wei, Xinyun Chen, Nicholas Carlini, and Dawn Song. 2017 · 2017
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
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Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection, See DBL 2018a
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Malware Detection by Eating a Whole EXE. In The Workshops of the The Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018. (AAAI Workshops) , Vol. WS-18. AAAI Press, 268–276
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Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers. In Research in Attacks, Intrusions, and Defenses - 21st International Symposium, RAID 2018, Heraklion, Crete, Greece, September 10-12, 2018, Proceedings (Lecture Notes in Computer Science) , Michael Bailey, Thorsten Holz, Manolis Stamatogiannakis, and Sotiris Ioannidis (Eds.), Vol. 11050. Springer, 490–510
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Adversarial Learning Attacks on Graph-based IoT Malware Detection Systems. In 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019, Dallas, TX, USA, July 7 , Vol. 10. 2019
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Characterizing and Evaluating Adversarial Examples for Offline Handwritten Signature Verification
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Black Box Attacks on Deep Anomaly Detectors. In Proceedings of the 14th International Conference on Availability, Reliability and Security (Canterbury, CA, United Kingdom) (ARES ’19) . ACM, New York, NY, USA, Article 21, 10 pages
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Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. In 2019 IEEE Symposium on Security and Privacy, SP 2019, San Francisco, CA, USA, May 19-23, 2019 . IEEE, 707–723
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Gray-box Adversarial Testing for Control Systems with Machine Learning Components. In Proceedings of the 22Nd ACM International Conference on Hybrid Systems: Computation and Control (Montreal, Quebec, Canada) (HSCC ’19) . ACM, New York, NY, USA, 179–184
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Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks. In Proceedings of the Sixth International Workshop on Security and Privacy Analytics . 53–60
Ahmed AlEroud and George Karabatis. 2020 · 2020
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Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security. In 2020 International Joint Conference on Neural Networks (IJCNN) . 1–8
A. Kuppa and N. A. Le-Khac. 2020 · 2020
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A Feature-vector Generative Adversarial Network for Evading PDF Malware Classifiers
Yuanzhang Li, Yaxiao Wang, Ye Wang, Lishan Ke, and Yu-an Tan. 2020b · 2020
Closest in time.
Global Texture Enhancement for Fake Face Detection in the Wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Zhengzhe Liu, Xiaojuan Qi, and Philip H.S. Torr. 2020 · 2020
Closest in time.
Bypassing NGAV for Fun and Profit
Ishai Rosenberg and Shai Meir. 2020 · 2020
Closest in time.
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability. In 2020 International Joint Conference on Neural Networks (IJCNN) . 1–10
Ishai Rosenberg, Shai Meir, Jonathan Berrebi, Ilay Gordon, Guillaume Sicard, and Eli (Omid) David. 2020a · 2020
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Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers. In ACSAC ’20: Annual Computer Security Applications Conference, Virtual Event / Austin, TX, USA, 7-11 December, 2020 . ACM, 611–626
Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, and Lior Rokach. 2020b · 2020
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MANIS: evading malware detection system on graph structure. In Proceedings of the 35th Annual ACM Symposium on Applied Computing . 1688–1695
Peng Xu, Bojan Kolosnjaji, Claudia Eckert, and Apostolis Zarras. 2020 · 2020
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