 | cloch001 wrote: Unsupervised Malware Detection: When Anomalies Aren't Rare
One of my models scored 0.350 ROC-AUC. That’s worse than random guessing, and I left it in the report.
This assignment used K-Means and DBSCAN to detect malware without labels. The models never saw which files were malicious. Labels came in onl (More) |
| cloch001 wrote: Malware Detection: The Perfect Score That Meant Nothing
Before I trained a single model for this assignment, the dataset already offered a perfect score.
The task was static malware detection. Each Windows executable had to be classified as malicious or benign from its Portable Executable (PE) header (More) |