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AuthorZaza, Ahmad M.N.
AuthorKharroub, Suleiman K.
AuthorAbualsaud, Khalid
Available date2024-03-26T11:56:48Z
Publication Date2020
Publication Name2020 IEEE 3rd 5G World Forum, 5GWF 2020 - Conference Proceedings
ResourceScopus
URIhttp://dx.doi.org/10.1109/5GWF49715.2020.9221100
URIhttp://hdl.handle.net/10576/53535
AbstractInternet of Things (IoT) is becoming more frequently used in more applications as the number of connected devices is in a rapid increase. More connected devices result in bigger challenges in terms of scalability, maintainability and most importantly security especially when it comes to 5G networks. The security aspect of IoT devices is an infant field, which is why it is our focus in this paper. Multiple IoT device manufacturers do not consider securing the devices they produce for different reasons like cost reduction or to avoid using energy-harvesting components. Such potentially malicious devices might be exploited by the adversary to do multiple harmful attacks. Therefore, we developed a system that can recognize malicious behavior of a specific IoT node on the network. Through convolutional neural network and monitoring, we were able to provide malware detection for IoT using a central node that can be installed within the network. The achievement shows how such models can be generalized and applied easily to any network while clearing out any stigma regarding deep learning techniques.
SponsorThis work was made possible by NPRP grant # 10-1205-160012 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectClassification
CNN
Internet of Things (IoT)
Machine Learning
Malware
Multicategorial
TitleLightweight IoT Malware Detection Solution Using CNN Classification
TypeConference Paper
Pagination212-217
dc.accessType Abstract Only


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