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CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls
May 7, 2024, 4:43 a.m. | Ahmed Bensaoud, Jugal Kalita
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we propose a novel model for a malware classification system based on Application Programming Interface (API) calls and opcodes, to improve classification accuracy. This system uses a novel design of combined Convolutional Neural Network and Long Short-Term Memory. We extract opcode sequences and API Calls from Windows malware samples for classification. We transform these features into N-grams (N = 2, 3, and 10)-gram sequences. Our experiments on a dataset of 9,749,57 samples …
abstract accuracy api application application programming interface arxiv classification cnn convolutional convolutional neural network cs.ai cs.cr cs.lg design long short-term memory lstm malware malware classification memory network neural network novel paper programming transfer transfer learning type
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