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Despite the demonstrated effectiveness of transformer models in NLP, andimage and video classification, the available tools for extracting featuresfrom captured IoT network flow packets fail to capture sequential patterns inaddition to the absence of spatial patterns consequently limiting transformermodel application. This work introduces a novel preprocessing method to adapttransformer models, the vision transformer (ViT) in particular, for IoT botnetattack detection using network flow packets. The approach involves featureextraction from .pcap files and transforming each instance into a 1-channel 2Dimage shape, enabling ViT-based classification. Also, the ViT model wasenhanced to allow use any classifier besides Multilayer Perceptron (MLP) thatwas deployed in the initial ViT paper. Models including the conventional feedforward Deep Neural Network (DNN), LSTM and Bidirectional-LSTM (BLSTM)demonstrated competitive performance in terms of precision, recall, andF1-score for multiclass-based attack detection when evaluated on two IoT attackdatasets.

IoT Botnet Detection: Application of Vision Transformer to Classification of Network Flow Traffic