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.

嵌入式计算系统的关键安全威胁之一是恶意软件，近期机器学习已被广泛应用于恶意软件检测，但现有技术需要大量良性和恶意样本来训练有效的恶意软件检测器。为解决此问题，我们引入了一种基于代码感知的数据生成技术，通过设备生成多个有限见过的恶意软件的变异样本，从而在训练集中能够高效检测新出现的恶意软件。实验结果表明，该技术能够以 90% 的准确率检测有限见过的恶意软件，比现有技术的准确率高出约 3 倍。