Design of an integrated evidence-driven few-shot meta-learning for zero-day malware detection and forensic attributions (opens in new tab)

Zero-day malware still slips past the best detection systems because most models need thousands of labeled examples before they learn anything useful. That dependency is exactly the weak point: by the time enough samples accumulate, the damage is already spreading. Traditional few-shot approaches promise quicker adaptation, yet they often reduce rich forensic evidence into flat feature vectors and end up overfitting to byte-level quirks rather than behavioral signals. This work takes a differ...