Optimization of Lightweight Malware Detection Models For AIoT Devices

arXiv:2404.04567v1 Announce Type: new
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 …

artificial artificial intelligence artificial intelligence of things arxiv can compromised connected connected devices cs.cr cs.lg detection devices ecosystem home intelligence internet internet of things intrusion iot machine machine learning malware malware detection optimization smart smart home things