I'm a 6th semester CS student at COMSATS University Islamabad. Over the past few months I've been doing deep learning research alongside my coursework, and we've submitted two papers to IEEE Access. Here's what we built and what I learned. Paper 1 — IoT Intrusion Detection IoT devices get attacked constantly. The problem with most deep learning IDS research is that models are validated on one dataset and never tested anywhere else — so you don't actually know if they generalize. We took the CNN-DNN architecture from Nazari et al. (validated on Kitsune) and re-evaluated it on CICIDS-2017 — 2.8 million network flow records, 12 attack classes. It didn't just hold up, it improved: from 98.47% to 99.50% accuracy. That's the generalizability proof the original paper left open. We also benchmarked a Transformer. Theoretically appealing for traffic classification — self-attention should model complex feature dependencies well.…