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- W4361732912 abstract "Malware is a major security threat to computer systems and significantly impacts system reliability. Recurrent neural network (RNN)-based methods have attracted much attention in API call-based malware detection in recent decades. However, traditional RNNs have a gradient vanishing problem when processing long API call sequences. This paper proposes a transformer encoder-based model, called MalTransEn, to solve the limitations of RNN. In particular, a novel transformer encoder-based classifier is proposed to classify malware by learning interaction features in sequences of API calls. Experimental results showed that the proposed architecture performed well and outperformed other deep learning-based baselines." @default.
- W4361732912 created "2023-04-04" @default.
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- W4361732912 date "2022-12-01" @default.
- W4361732912 modified "2023-10-16" @default.
- W4361732912 title "An Efficient Transformer Encoder-Based Classification of Malware Using API Calls" @default.
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- W4361732912 doi "https://doi.org/10.1109/hpcc-dss-smartcity-dependsys57074.2022.00137" @default.
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