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Secure Kernel Machines against Evasion Attacks
Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, 2016Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary-aware learning algorithms have been developed, exploiting robust optimization and game-theoretical models to ...
RUSSU, PAOLO +4 more
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Evasion Attack of Multi-class Linear Classifiers
2012Machine learning has yield significant advances in decision-making for complex systems, but are they robust against adversarial attacks? We generalize the evasion attack problem to the multi-class linear classifiers, and present an efficient algorithm for approximating the optimal disguised instance.
Han Xiao 0002 +2 more
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On the Possibility of Evasion Attacks with Macro Malware
2021To detect unknown malware, several methods with machine learning techniques have been proposed. These methods extract the features of malware, and thereby classify samples as malicious or benign. The previous method extracts words from the source code and detects macro malware.
Risa Yamamoto, Mamoru Mimura
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Complex-based optimization strategy for evasion attack
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017Machine learning has been widely used in security related applications, such as spam filter, network intrusion detection. In machine learning process, the test set and the training set usually have the same probability distribution and through the information of learning the training set, the malicious samples in the machine learning algorithm can ...
Shu Li, Yun Li
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Evasion attacks on ML in domains with nonlinear constraints
2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)Cybersecurity efforts aim to protect computer sys- tems, especially complex systems with dynamically changing architectures like mobile computational clouds, from digital attacks. The use of machine learning (ML) models in these systems to analyze and process data, allocate resources or monitor and manage infrastructure is increasingly the field of ...
Krzysztoń, Mateusz +3 more
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Robust Data-Driven Detection of Electricity Theft Adversarial Evasion Attacks in Smart Grids
IEEE Transactions on Smart Grid, 2023Erchin Serpedin +2 more
exaly
Adversarial Ensemble Modeling for Evasion Attack Detection
2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2022Usman Ahmed, Jerry Chun-Wei Lin
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Adversarial Machine Learning Protection Using the Example of Evasion Attacks on Medical Images
Automatic Control and Computer Sciences, 2023Maria Poltavtseva
exaly
A Generative Model for Evasion Attacks in Smart Grid
IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2022Venkata Praveen Kumar Madhavarapu +2 more
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