Beyond Text: Hacking Transformers to Detect Anomalies in Million-Scale Netflow Data

Everyone uses Transformers for chat, but I wanted to see if they could catch hackers. In this demo, I’ll show how I forced NLP models (like ELECTRA) to ‘read’ & classify network traffic by treating diverse IoT datasets as text, graph-embedding, and even quantum-encoding.
I’ll skip the slides and scroll through my Colab notebooks to show the messy reality of this experiment. You’ll see the custom data transformation scripts I wrote to tokenize IP addresses and build traffic graphs, the model definitions where I adapted the Transformer architectures, and the final code module that fuses these four wild modalities together to outperform standard detection methods.