Covering Arrays ML HPO for Static Malware Detection

Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) novel approach that can enhance machine learning classifier accuracy. The tuning of (ML) classifiers to increase accuracy needed nowadays, especially with newly evolving malware. Four techniques were tuned cAgen, tool for generating arrays. results show cAgen an efficient achieve the optimal parameter choices ML techniques. Moreover, array shows significant promise, regard hyper-parameter community, malware detectors community and overall security testing. This research will aid adding better static PE detection.