Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many domains. However, these IoT devices can also contain security vulnerabilities, which allow attackers to compromise them using malware. We report on our recent work on using machine learning for efficient and effective malware detection on resource-constrained IoT devices.
BibTeX:
@Article{BuF22,
author = {Buttyán, Levente and Ferenc, Rudolf},
journal = {ERCIM NEWS},
title = {IoT Malware Detection with Machine Learning},
year = {2022},
month = apr,
note = {Open Access},
pages = {17-19},
volume = {129},
doi = {10.3390/technologies9010003},
keywords = {bug prediction, hybrid code analysis, call-graph, source code metrics},
url = {https://ercim-news.ercim.eu/en129/special/iot-malware-detection-with-machine-learning},
}