The moment this model hit live traffic and stumbled, it wasn't a failure. It was the first honest signal in a project that had been lying to itself. High validation accuracy on an imbalanced, self-generated dataset is not proof of a working detection system. It is proof that the model learned to exploit the path of least resistance: predict "attack" often enough, and the metrics will look great until reality interrupts. This builder did the right thing by testing in the same lab, with unseen traffic, and letting the results break the illusion. That is not a setback. That is the entire point of building a serious practical project.
For anyone walking this same path, the lesson is not about tweaking hyperparameters or chasing a better accuracy score. It is about understanding that your dataset is a mirror of your assumptions. If normal traffic is thin, repetitive, or too clean, your model will never learn to recognize the messy, varied, and sometimes ambiguous behavior of real users. The imbalance here is not just a statistical nuisance. It is a direct consequence of how the lab was built. Attack traffic is easy to generate in bulk. Normal traffic requires patience, intentionality, and a willingness to simulate the boring, irregular, and occasionally wasteful actions of actual humans. That is the coverage that makes a model robust.
The community questions this builder is asking are the right ones, but the order matters. Before worrying about XGBoost versus LightGBM or whether to switch to NetFlow-style features, fix the foundation. A balanced dataset with strong normal traffic coverage will do more for a Random Forest than a fancier algorithm will do for a lopsided one. And on evaluation: do not rely on a single split. Use time-based splits, simulate live conditions, and track precision and recall on the minority class, because that is where the real cost of failure lives. A model that misses normal traffic is annoying. A model that misses an attack is a liability.
The fact that this project is a small lab setup does not make the lesson small. It makes it more valuable, because the stakes are lower and the feedback loop is faster. This is how real skill is built: not by celebrating a strong training score, but by chasing the gap between what works in simulation and what survives contact with live traffic. The next iteration will be better because this one failed honestly. That is the only kind of progress that matters in applied machine learning.