Perturbation Attacks refer to a set of malicious alterations made to the input data of machine learning models, primarily aimed at misleading the models into ...
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Malicious data injection attacks, which alter the values of measurements without being detected, are one potential cause of bad data and may have serious ...
Feb 29, 2024 · adversarial attacks, malicious perturbations should be fully considered as well as benign perturbations in the training of. AT methods ...
May 30, 2022 · However, the introduction of malicious perturbations in input data, known as adversarial examples, has been shown to deceive neural network ...
In this paper, we propose a novel framework, learning to discriminate perturbations (DISP), to identify and adjust malicious perturbations, thereby blocking ...
In this context, UAPs enable attackers to cheaply reuse the same collection of predefined perturbations and evade detection for different types of input malware ...
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Learning to Discriminate Perturbations for ... - UCLA Computer Science
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In this paper, we propose a novel framework, learning to DIScriminate Perturbations (DISP), to identify and adjust malicious perturbations, thereby blocking ...
A common cybersecurity application is malware detection, which identifies if an executable file is benign or malicious. Unlike images, diverse and semantically ...
... malicious perturbations and naturally occurring fog phenomena. To the best of our knowledge, we are the first to explicitly employ a natural perturbation ...
Abstract. Bad measurement data exists in power systems for a number of reasons. Malicious data injection attacks, which alter the values of measurements ...