Hi everyone, I’m an IT student currently planning my final year project and I’d really appreciate some honest feedback from people with more experience.
I’m thinking of building an AI-based wireless attack detection system focused on detecting common WiFi threats like deauthentication and evil twin attacks. The idea is to capture wireless traffic using a WiFi adapter in monitor mode (via packet capture tools), and possibly extend it to Layer 1 using SDR to capture raw RF signals as well.
For the ML part, I’m planning to convert the captured data into image-like representations (e.g., spectrograms or feature maps) and use a Vision Transformer (ViT) model to classify whether the traffic is normal or an attack. The system would then display real-time alerts and logs on a desktop dashboard, so it can actually demonstrate detection live during evaluation.
My goal is to build something that’s:
• Technically strong (not just a simple web/app project)
• Based on real-world cybersecurity problems
• Fully working in a live demo (e.g., detecting a real deauth attack)
• Competitive enough for top project awards
At the same time, I’m a bit concerned about the scope and practicality.
So I wanted to ask:
• Is this too ambitious for an undergraduate project (especially with SDR + AI combined)?
• Does using a Vision Transformer make sense for this type of data, or would a simpler ML model be more practical?
• Would it be smarter to limit the scope to Layer 2 only (WiFi attacks) and skip SDR?
• Are there better approaches, tools, or ideas you’d recommend for this kind of project?
I’d really appreciate honest feedback, even if it’s critical. I’d rather adjust early than struggle later. Thanks