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- W2938888905 abstract "Ransomware, as a specialized form of malicious software, has recently emerged as a major threat in computer security. With an ability to lock out user access to their content, recent ransomware attacks have caused severe impact at an individual and organizational level. While research in malware detection can be adapted directly for ransomware, specific structural properties of ransomware can further improve the quality of detection. In this paper, we adapt the deep learning methods used in malware detection for detecting ransomware from emulation sequences. We present specialized recurrent neural networks for capturing local event patterns in ransomware sequences using the concept of attention mechanisms. We demonstrate the performance of enhanced LSTM models on a sequence dataset derived by the emulation of ransomware executables targeting the Windows environment." @default.
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- W2938888905 date "2019-05-01" @default.
- W2938888905 modified "2023-10-18" @default.
- W2938888905 title "Attention in Recurrent Neural Networks for Ransomware Detection" @default.
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- W2938888905 doi "https://doi.org/10.1109/icassp.2019.8682899" @default.
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