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。

嵌入式计算系统的有效恶意软件检测

Effective Malware Detection for Embedded Computing Systems with Limited  Exposure

In this paper, we explore how to design lightweight CNN architecture for
embedded computing systems. We propose L-Mobilenet model for ZYNQ based
hardware platform. L-Mobilenet can adapt well to the hardware computing and
accelerating, and its network structure is inspired by the state-of-the-art
work of Inception-ResnetV1 and MobilenetV2, which can effectively reduce
parameters and delay while maintaining the accuracy of inference. We deploy our
L-Mobilenet model to ZYNQ embedded platform for fully evaluating the
performance of our design. By measuring in cifar10 and cifar100 datasets,
L-Mobilenet model is able to gain 3x speed up and 3.7x fewer parameters than
MobileNetV2 while maintaining a similar accuracy. It also can obtain 2x speed
up and 1.5x fewer parameters than ShufflenetV2 while maintaining the same
accuracy. Experiments show that our network model can obtain better performance
because of the special considerations for hardware accelerating and
software-hardware co-design strategies in our L-Mobilenet bottleneck
architecture.

本研究探索如何为嵌入式计算系统设计轻量级 CNN 架构，提出了适用于基于 ZYNQ 硬件平台的 L-Mobilenet 模型。L-Mobilenet 可以很好地适应硬件计算和加速，并且其网络结构受到 Inception-ResnetV1 和 MobilenetV2 等最先进技术的启发，能够有效减少参数和延迟，同时保持推理的准确性。通过在 cifar10 和 cifar100 数据集上进行测量，L-Mobilenet 模型相比 MobileNetV2 速度提升了 3 倍，参数减少了 3.7 倍，同时保持了类似的准确性。相较于 ShufflenetV2，L-Mobilenet 模型速度提升了 2 倍，参数减少了 1.5 倍，同时保持了相同的准确性。实验证明，由于对硬件加速和软硬件协同设计策略的特殊考虑，我们的网络模型能够获得更好的性能。