API Call-Based Malware Classification Using Recurrent Neural Networks
نویسندگان
چکیده
Malicious software, called malware, can perform harmful actions on computer systems, which may cause economic damage and information leakage. Therefore, malware classification is meaningful required to prevent attacks. Application programming interface (API) call sequences are easily observed good choices as features for classification. However, one of the main issues how generate a suitable feature algorithms achieve high accuracy. Different sample brings API sequence with different lengths, these lengths reach millions, computation cost time complexities. Recurrent neural networks (RNNs) most versatile approaches process series data, be used call-based Malware calssification. In this paper, we propose model RNN, especially long short-term memory (LSTM) gated recurrent unit (GRU), classify variants by using long-sequences calls. numerical experiments, benchmark dataset illustrate proposed approach validate its The results show that RNN works well
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ژورنال
عنوان ژورنال: Journal of cyber security and mobility
سال: 2021
ISSN: ['2245-1439', '2245-4578']
DOI: https://doi.org/10.13052/jcsm2245-1439.1036