Topological safeguard for evasion attack interpreting the neural networks’ behavior
Pattern Recognition, ISSN: 0031-3203, Vol: 147, Page: 110130
2024
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New Findings from Vicomtech Foundation Describe Advances in Networks (Topological Safeguard for Evasion Attack Interpreting the Neural Networks' Behavior)
2024 FEB 29 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- A new study on Networks is now available. According
Article Description
In the last years, Deep Learning technology has been proposed in different fields, bringing many advances in each of them, but raising new threats in these solutions regarding cybersecurity. Those implemented models have brought several vulnerabilities associated with Deep Learning technology. Moreover, those allow taking advantage of the implemented model, obtaining private information, and even modifying the model’s decision-making. Therefore, interest in studying those vulnerabilities/attacks and designing defenses to avoid or fight them is gaining prominence among researchers. In particular, the widely known evasion attack is being analyzed by researchers; thus, several defenses to avoid such a threat can be found in the literature. Since the presentation of the L-BFG algorithm, this threat concerns the research community. However, it continues developing new and ingenious countermeasures since there is no perfect defense for all the known evasion algorithms. In this work, a novel detector of evasion attacks is developed. It focuses on the information on the activations of the neurons given by the model when an input sample is injected. Moreover, it pays attention to the topology of the targeted deep learning model to analyze the activations according to which neurons are connecting. This approach is motivated from the observation that the literature shows that the targeted model’s topology contains essential information about if the evasion attack occurs. For this purpose, a huge data preprocessing is required to introduce all this information in the detector, which uses the Graph Convolutional Neural Network (GCN) technology. Thus, it understands the topology of the target model, obtaining promising results and improving the outcomes presented in the literature related to similar defenses.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0031320323008270; http://dx.doi.org/10.1016/j.patcog.2023.110130; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85177816866&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0031320323008270; https://dx.doi.org/10.1016/j.patcog.2023.110130
Elsevier BV
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