Generative AI-Based Effective Malware Detection for Embedded Computing Systems
This addresses a critical security issue for embedded computing systems by improving malware detection with limited data, though it is incremental as it builds on existing ML approaches.
The paper tackles the problem of detecting emerging malware in embedded systems with limited training samples by introducing a code-aware data generation technique that creates mutated malware samples to augment training, achieving 90% accuracy which is three times higher than state-of-the-art methods.
One of the pivotal security threats for the embedded computing systems is malicious software a.k.a malware. With efficiency and efficacy, Machine Learning (ML) has been widely adopted for malware detection in recent times. Despite being efficient, the existing techniques require a tremendous number of benign and malware samples for training and modeling an efficient malware detector. Furthermore, such constraints limit the detection of emerging malware samples due to the lack of sufficient malware samples required for efficient training. To address such concerns, we introduce a code-aware data generation technique that generates multiple mutated samples of the limitedly seen malware by the devices. Loss minimization ensures that the generated samples closely mimic the limitedly seen malware and mitigate the impractical samples. Such developed malware is further incorporated into the training set to formulate the model that can efficiently detect the emerging malware despite having limited exposure. The experimental results demonstrates that the proposed technique achieves an accuracy of 90% in detecting limitedly seen malware, which is approximately 3x more than the accuracy attained by state-of-the-art techniques.