Optimization of Lightweight Malware Detection Models For AIoT Devices
Felicia Lo Shin-Ming Cheng Rafael Kaliski
Malware intrusion is problematic for Internet of Things (IoT) and Artificial
Intelligence of Things (AIoT) devices as they often reside in an ecosystem of
connected devices, such as a smart home. If any devices are infected, the whole
ecosystem can be compromised. Although various Machine Learning (ML) models are
deployed to detect malware and network intrusion, generally speaking, robust
high-accuracy models tend to require resources not found in all IoT devices,
compared to less robust models defined by weak learners. In order to combat
this issue, Fadhilla proposed a meta-learner ensemble model comprised of less
robust prediction results inherent with weak learner ML models to produce a
highly robust meta-learning ensemble model. The main problem with the prior
research is that it cannot be deployed in low-end AIoT devices due to the
limited resources comprising processing power, storage, and memory (the
required libraries quickly exhaust low-end AIoT devices' resources.) Hence,
this research aims to optimize the proposed super learner meta-learning
ensemble model to make it viable for low-end AIoT devices. We show the library
and ML model memory requirements associated with each optimization stage and
emphasize that optimization of current ML models is necessitated for low-end
AIoT devices. Our results demonstrate that we can obtain similar accuracy and
False Positive Rate (FPR) metrics from high-end AIoT devices running the
derived ML model, with a lower inference duration and smaller memory footprint.