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Adversarial Machine Learning for Text

Proceedings of the Sixth International Workshop on Security and Privacy Analytics, 2020
In this tutorial, we investigate the history, evolution and latest research topics in the area of adversarial machine learning for text data. Both classical attacks on spam filters and more recent attacks on deep learning models for text classification problems will be discussed. We then discuss proposed and potential defenses against these attacks. We
Daniel Lee, Rakesh Verma
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Machine learning in adversarial environments

Machine Learning, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Laskov, Pavel, Lippmann, Richard
openaire   +2 more sources

Adversarial Machine Learning and Explainability

2021
Do you see any difference between these two pandas? I bet the answer is no; we don’t have any doubt on saying that both of them represent a panda. But as shown by Goodfellow et al. (2014), the first one has been classified as a panda by a NN with 55.7% confidence, while the second has been classified by the same NN as a gibbon with 99.3% confidence ...
Leonida Gianfagna, Antonio Di Cecco
openaire   +1 more source

Adversarial Machine Learning in Cybersecurity

With the evolution and penetration of AI and ML into almost all critical public life domains including cybersecurity, the cybercrime ecosystem attempts to tap the vulnerabilities in AI-based cybersecurity systems by invoking adversarial machine learning, which has posed a significant challenge to cyber-physical security systems employing ML.
Vikram Singh, Sanyogita Singh
openaire   +1 more source

Adversarial Machine Learning in Cybersecurity

2019
Adversarial machine learning algorithms deal with adversarial sample generation which is creating false input data that are capable enough to fool any machine learning model. For instance, attributes of a goodware can be added to a malware executable to make the classifier identify a malicious sample as benign.
Tony Thomas   +2 more
openaire   +1 more source

Adversarial Machine Learning

2018
Yevgeniy Vorobeychik, Murat Kantarcioglu
openaire   +2 more sources

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

IEEE Communications Surveys and Tutorials, 2023
Ke He   +2 more
exaly  

A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security

IEEE Communications Surveys and Tutorials, 2023
Senming Yan, Jing Ren, Wei Wang
exaly  

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