Effective One-Class Classifier Model for Memory Dump Malware Detection

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware gaining increased attention due to its ability expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based One class SVM (OCSVM) classifier that can identify any deviation from the normal memory file patterns and detect it as malware. The proposed integrates OCSVM Principal Component Analysis (PCA) for sensitivity efficiency. An up-to-date dataset known “MALMEMANALYSIS-2022” was utilized during evaluation phase of this study. accuracy achieved by traditional one-class (TOCC) 55%, compared 99.4% in with PCA (OCC-PCA) model. Such results have confirmed improved performance