The paper addresses the challenge of detecting malware in embedded computing systems, where there is limited exposure to malware samples. The key highlights are:
The authors introduce a code-aware data generation technique that generates mutated samples of the limitedly seen malware. This helps mitigate the need for a large training dataset.
Loss minimization is employed to ensure the generated samples closely mimic the features and functionality of the limited malware data.
Few-shot learning is used to efficiently classify complex stealthy malware and code obfuscated malware, even with limited training samples.
The proposed approach can achieve up to 89.52% accuracy in detecting complex malware, which is 7% higher compared to models trained only on limited samples. The authors also provide ASIC implementation results for different classifier models, demonstrating the efficiency of the proposed technique.
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