CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls
<|reference_start|>CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls: 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 produce high accuracy of 99.91% using the 8-gram sequences. Our method significantly improves the malware classification performance when using a wide range of recent deep learning architectures, leading to state-of-the-art performance. In particular, we experiment with ConvNeXt-T, ConvNeXt-S, RegNetY-4GF, RegNetY-8GF, RegNetY-12GF, EfficientNetV2, Sequencer2D-L, Swin-T, ViT-G/14, ViT-Ti, ViT-S, VIT-B, VIT-L, and MaxViT-B. Among these architectures, Swin-T and Sequencer2D-L architectures achieved high accuracies of 99.82% and 99.70%, respectively, comparable to our CNN-LSTM architecture although not surpassing it.<|reference_end|>
@article{bensaoud2024cnn-lstm,
    title={CNN-LSTM and Transfer Learning Models for Malware Classification based
  on Opcodes and API Calls},
    author={Ahmed Bensaoud, Jugal Kalita},
    journal={Bensaoud, A., & Kalita, J. (2024). CNN-LSTM and transfer learning
  models for malware classification based on opcodes and API calls.
  Knowledge-Based Systems, 111543},
    year={2024},
    doi={10.1016/j.knosys.2024.111543},
    archivePrefix={arXiv},
    eprint={2405.02548},
    primaryClass={cs.CR cs.AI cs.LG}
}