Structural Feature Engineering approach for detecting polymorphic malware - Masabo, Emmanuel; Kaawaase, Kyanda Swaib; Sansa-Otim, Julianne; Hanyurwimfura, Damien (IEEE, 2017)
Currently, malware are distributed in a polymorphic form. There are very smart and obfuscated. This serves the purpose of hardening detection or simply making it impossible. Researchers have mainly resorted to static analysis, dynamic analysis or a combination of both in attempting to find advanced solutions to polymorphic malware detection problems. This paper presents a novel simple feature engineering approach in terms of extracting, analyzing and processing static based features for efficient detection of polymorphic malware. K-NN algorithm is used to build the detection model. Our experiments achieve a detection accuracy of 98.7% with 0.014% False Positive Rate (FPR) on a relatively small dataset.
Keywords: Polymorphic malware; Static analysis; Machine learning