Volume 6 , Issue 2 , PP: 43-55, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Denis A. Pustokhin 1 * , Irina V. Pustokhina 2
Doi: https://doi.org/10.54216/IJWAC.060204
In the past few years, billions of Internet of Things (IoT) devices that lacked adequate security procedures were created and deployed, and more of these devices are on the way as a result of the development of Beyond 5G technologies. Because of their susceptibility to malware, there is a pressing need for reliable methods that can identify infected IoT devices within networks. Precise and early identification of IoT malware is inevitable to achieve IoT security. Nevertheless, prevailing studies of IoT malware detection mostly support certain platforms, need complicated deep learning (DL) models to achieve efficiency, and are centrally trained on the device. The purpose of this study is to introduce a new Federated Learning (FL) Framework, which has been given the name FLC-NET, in order to train numerous distributed edge devices to identify malware cooperatively. After the malware binaries have been encoded into image representations using FLC-NET, a lightweight convolutional network known as LC-NET is introduced to model these malware patterns directly from the image data without any data engineering being required. Because of its lightweight design, LC-NET is suited for use in devices with limited resource availability. After that, sophisticated adversarial training will be offered on FLC-NET in order to collect defensive knowledge against adversarial samples from a variety of clients who will be participating. The FLC-NET is experimentally evaluated on the public malware dataset, and it is demonstrated efficient (Accuracy: 96.1%, f1-score: 95.5), effective, scalable, and resistant to adversarial attacks.
Malware Detection , Federated Learning , Deep Learning , Edge/Fog Computing , adversarial attacks
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