ViT-EBoT: Vision Transformer for Encrypted Botnet Detection in Resource-Constrained Edge Devices
Abstract: With the advent of lightweight cryptography in edge devices, attackers can hide malicious code under encrypted network communications to perform malware attacks. This makes IoT botnet attacks extremely challenging to detect by means of traditional signature-based techniques. In this paper, we propose a novel IoT botnet detection framework that uses vision transformers to detect malicious communications captured in encrypted network flow images. Our approach achieved ∼98% accuracy and around 94% reduced inference latency compared to state-of-the-art approaches. Further, we have validated the practicality of our approach by testing it on Jetson Orin Nano acting as an edge gateway and achieved reduced inference latency of 25.16 ms and area overhead of 88.13 MB.
Submission Length: Long submission (more than 12 pages of main content)
Assigned Action Editor: ~Sanghyun_Hong1
Submission Number: 4016
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