Optimization of Lightweight Malware Detection Models For AIoT Devices

• URL: http://arxiv.org/abs/2404.04567v1
• Date: Sat, 6 Apr 2024 09:30:38 GMT
• Title: Optimization of Lightweight Malware Detection Models For AIoT Devices
• Authors: Felicia Lo, Shin-Ming Cheng, Rafael Kaliski,
• Abstract summary: Malware intrusion is a problem for Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) devices. This research aims to optimize the proposed super learner meta-learning ensemble model to make it viable for low-end AIoT devices.
• Score: 2.4947404267499587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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