Fetching the paper…
Reading the bibliography…
As advances in Deep Neural Networks (DNNs) demonstrate unprecedented levels of performance in many critical applications, their vulnerability to attacks is still an open question.
Adversarial Network Traffic: Towards Evaluating the Robustness of Deep-Learning-Based Network Traffic Classification
Amir Mahdi Sadeghzadeh, Saeed Shiravi, and Rasool Jalili. 2021 · 1976
Earlier work this paper cites.
Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs. In Proc. 15th ACM International Conference on Knowledge Discovery and Data Mining (KDD)
Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. 2009 · 2009
Earlier work this paper cites.
EXPOSURE: Finding Malicious Domains Using Passive DNS Analysis. In Proc. 18th Symposium on Network and Distributed System Security (NDSS)
Leyla Bilge, Engin Kirda, Kruegel Christopher, and Marco Balduzzi. 2011 · 2011
Earlier work this paper cites.
Adversarial machine learning. In Proceedings of the 4th ACM workshop on Security and artificial intelligence . ACM, 43–58
Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and JD Tygar. 2011 · 2011
Earlier work this paper cites.
Poisoning attacks against support vector machines. In ICML
Battista Biggio, Blaine Nelson, and Pavel Laskov. 2012 · 2012
Earlier work this paper cites.
Evasion Attacks against Machine Learning at Test Time. In Proc. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli. 2013 · 2013
Earlier work this paper cites.
An empirical comparison of botnet detection methods
Sebastian Garcia, Martin Grill, Jan Stiborek, and Alejandro Zunino. 2014 · 2014
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
Practical Evasion of a Learning-Based Classifier: A Case Study. In Proc. IEEE Security and Privacy Symposium
Nedim Srndic and Pavel Laskov. 2014 · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. In Proceedings of the 22nd ACM Conference on Computer and Communications Security (CCS)
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
Earlier work this paper cites.
Is feature selection secure against training data poisoning?. In Proc. 32nd International Conference on Machine Learning (ICML, Vol. 37) . 1689–1698
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, and Fabio Roli. 2015 · 2015
Earlier work this paper cites.
Optimized Invariant Representation of Network Traffic for Detecting Unseen Malware Variants. In 25th USENIX Security Symposium (USENIX Security 16) . USENIX Association, 807–822
Karel Bartos, Michal Sofka, and Vojtech Franc. 2016 · 2016
Earlier work this paper cites.
Adversarial perturbations against deep neural networks for malware classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel. 2016 · 2016
Earlier work this paper cites.
BAYWATCH: Robust Beaconing Detection to Identify Infected Hosts in Large-Scale Enterprise Networks. In DSN . IEEE Computer Society, 479–490
Xin Hu, Jiyong Jang, Marc Ph. Stoecklin, Ting Wang, Douglas Lee Schales, Dhilung Kirat, and Josyula R. Rao. 2016 · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016 · 2016
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. 2016 · 2016
Earlier work this paper cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. 2016a · 2016
Earlier work this paper cites.
Crafting adversarial input sequences for recurrent neural networks. In MILCOM 2016-2016 IEEE Military Communications Conference . IEEE, 49–54
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang. 2016b · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Automatically evading classifiers. In Proceedings of the 2016 Network and Distributed Systems Symposium . 21–24
Weilin Xu, Yanjun Qi, and David Evans. 2016 · 2016
Earlier work this paper cites.
Towards Evaluating the Robustness of Neural Networks. In Proc. IEEE Security and Privacy Symposium
Nicholas Carlini and David Wagner. 2017 · 2017
Earlier work this paper cites.
Adversarial machine learning in malware detection: Arms race between evasion attack and defense. In 2017 European Intelligence and Security Informatics Conference (EISIC) . IEEE, 99–106
Lingwei Chen, Yanfang Ye, and Thirimachos Bourlai. 2017 · 2017
Earlier work this paper cites.
Houdini: Fooling deep structured prediction models
Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet. 2017 · 2017
Earlier work this paper cites.
Evading classifiers by morphing in the dark. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 119–133
Hung Dang, Yue Huang, and Ee-Chien Chang. 2017 · 2017
Earlier work this paper cites.
Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2017 · 2017
Earlier work this paper cites.
Crafting adversarial examples for speech paralinguistics applications
Yuan Gong and Christian Poellabauer. 2017 · 2017
Earlier work this paper cites.
On the (Statistical) Detection of Adversarial Examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. 2017 · 2017
Cited alongside, same era.
Attacking automatic video analysis algorithms: A case study of google cloud video intelligence api. In Proceedings of the 2017 on Multimedia Privacy and Security . ACM, 21–32
Hossein Hosseini, Baicen Xiao, Andrew Clark, and Radha Poovendran. 2017 · 2017
Cited alongside, same era.
Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi. 2017 · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
Cited alongside, same era.
Robust audio adversarial example for a physical attack
Hiromu Yakura and Jun Sakuma. 2018 · 2018
Later among the works it cites.
Adversarial examples against the deep learning based network intrusion detection systems. In MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM) . IEEE, 559–564
Kaichen Yang, Jianqing Liu, Chi Zhang, and Yuguang Fang. 2018b · 2018
Later among the works it cites.
Characterizing audio adversarial examples using temporal dependency
Zhuolin Yang, Bo Li, Pin-Yu Chen, and Dawn Song. 2018a · 2018
Later among the works it cites.
Examining the robustness of learning-based ddos detection in software defined networks. In 2019 IEEE Conference on Dependable and Secure Computing (DSC) . IEEE, 1–8
Ahmed Abusnaina, Aminollah Khormali, DaeHun Nyang, Murat Yuksel, and Aziz Mohaisen. 2019 · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maria Rigaki. 2017 · 2017
Cited alongside, same era.
Membership Inference Attacks against Machine Learning Models. In Proc. IEEE Security and Privacy Symposium (S&P)
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Cited alongside, same era.
Liwei Song and Prateek Mittal. 2017 · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2017 · 2017
Cited alongside, same era.
Dolphinattack: Inaudible voice commands. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 103–117
Guoming Zhang, Chen Yan, Xiaoyu Ji, Tianchen Zhang, Taimin Zhang, and Wenyuan Xu. 2017 · 2017
Cited alongside, same era.
Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018 · 2018
Cited alongside, same era.
Learning to evade static PE machine learning malware models via reinforcement learning
Hyrum S Anderson, Anant Kharkar, Bobby Filar, David Evans, and Phil Roth. 2018 · 2018
Cited alongside, same era.
Evading Botnet Detectors Based on Flows and Random Forest with Adversarial Samples. 1–8
Giovanni Apruzzese and Michele Colajanni. 2018 · 2018
Cited alongside, same era.
Joseph Clements, Yuzhe Yang, Ankur Sharma, Hongxin Hu, and Yingjie Lao. 2019 · 2019
Closest in time.
Certified adversarial robustness via randomized smoothing. In International Conference on Machine Learning . PMLR, 1310–1320
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. 2019 · 2019
Closest in time.
Adversarial Machine Learning for Cyber Security
Michael J De Lucia and Chase Cotton. 2019 · 2019
Closest in time.
Why do adversarial attacks transfer? Explaining transferability of evasion and poisoning attacks. In 28th USENIX Security Symposium (USENIX Security 19) . 321–338
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, and Fabio Roli. 2019 · 2019
Closest in time.
Analyzing the footprint of classifiers in adversarial denial of service contexts. In EPIA Conference on Artificial Intelligence . Springer, 256–267
Nuno Martins, José Magalhães Cruz, Tiago Cruz, and Pedro Henriques Abreu. 2019 · 2019
Closest in time.
On Designing Machine Learning Models for Malicious Network Traffic Classification
Talha Ongun, Timothy Sakharaov, Simona Boboila, Alina Oprea, and Tina Eliassi-Rad. 2019 · 2019
Closest in time.
Intriguing Properties of Adversarial ML Attacks in the Problem Space
Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, and Lorenzo Cavallaro. 2019 · 2019
Closest in time.
Imperceptible, robust, and targeted adversarial examples for automatic speech recognition
Yao Qin, Nicholas Carlini, Ian Goodfellow, Garrison Cottrell, and Colin Raffel. 2019 · 2019
Closest in time.
The odds are odd: A statistical test for detecting adversarial examples
Kevin Roth, Yannic Kilcher, and Thomas Hofmann. 2019 · 2019
Closest in time.
A general framework for adversarial examples with objectives
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter. 2019 · 2019
Closest in time.
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features. In 28th USENIX Security Symposium (USENIX Security 19) . USENIX Association, Santa Clara, CA, 285–302
Liang Tong, Bo Li, Chen Hajaj, Chaowei Xiao, Ning Zhang, and Yevgeniy Vorobeychik. 2019 · 2019
Closest in time.
Evading machine learning botnet detection models via deep reinforcement learning. In ICC 2019-2019 IEEE International Conference on Communications (ICC) . IEEE, 1–6
Di Wu, Binxing Fang, Junnan Wang, Qixu Liu, and Xiang Cui. 2019 · 2019
Closest in time.
Automatically synthesizing DoS attack traces using generative adversarial networks
Qiao Yan, Mingde Wang, Wenyao Huang, Xupeng Luo, and F Richard Yu. 2019 · 2019
Closest in time.
Adversarial example detection and classification with asymmetrical adversarial training
Xuwang Yin, Soheil Kolouri, and Gustavo K Rohde. 2019 · 2019
Closest in time.
A new defense against adversarial images: Turning a weakness into a strength
Tao Yu, Shengyuan Hu, Chuan Guo, Wei-Lun Chao, and Kilian Q Weinberger. 2019 · 2019
Closest in time.
Adversarial machine learning in network intrusion detection systems
Elie Alhajjar, Paul Maxwell, and Nathaniel D Bastian. 2020 · 2020
Closest in time.
Generating Adversarial Examples against Machine Learning based Intrusion Detector in Industrial Control Systems
Jiming Chen, Xiangshan Gao, Ruilong Deng, Yang He, Chongrong Fang, and Peng Cheng. 2020 · 2020
Closest in time.
A Realistic Approach for Network Traffic Obfuscation Using Adversarial Machine Learning. In International Conference on Decision and Game Theory for Security . Springer, 45–57
Alonso Granados, Mohammad Sujan Miah, Anthony Ortiz, and Christopher Kiekintveld. 2020 · 2020
Closest in time.
Practical traffic-space adversarial attacks on learning-based nidss
Dongqi Han, Zhiliang Wang, Ying Zhong, Wenqi Chen, Jiahai Yang, Shuqiang Lu, Xingang Shi, and Xia Yin. 2020 · 2020
Closest in time.
Enhancing robustness against adversarial examples in network intrusion detection systems. In 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) . IEEE, 37–43
Mohammad J Hashemi and Eric Keller. 2020 · 2020
Closest in time.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry. 2020 · 2020
Closest in time.
MANDA: On Adversarial Example Detection for Network Intrusion Detection System. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications . IEEE, 1–10
Ning Wang, Yimin Chen, Yang Hu, Wenjing Lou, and Y Thomas Hou. 2021 · 2021
Closest in time.