Smart OMVI: Obfuscated Malware Variant Identification using a novel
  dataset
Suleman Qamar
Cybersecurity has become a significant issue in the digital era as a result
of the growth in everyday computer use. Cybercriminals now engage in more than
virus distribution and computer hacking. Cyberwarfare has developed as a result
because it has become a threat to a nation's survival. Malware analysis serves
as the first line of defence against an attack and is a significant component
of cybercrime. Every day, malware attacks target a large number of computer
users, businesses, and governmental agencies, causing billions of dollars in
losses. Malware may evade multiple AV software with a very minor, cunning tweak
made by its designers, despite the fact that security experts have a variety of
tools at their disposal to identify it. To address this challenge, a new
dataset called the Obfuscated Malware Dataset (OMD) has been developed. This
dataset comprises 40 distinct malware families having 21924 samples, and it
incorporates obfuscation techniques that mimic the strategies employed by
malware creators to make their malware variations different from the original
samples. The purpose of this dataset is to provide a more realistic and
representative environment for evaluating the effectiveness of malware analysis
techniques. Different conventional machine learning algorithms including but
not limited to Support Vector Machine (SVM), Random Forrest (RF), Extreme
Gradient Boosting (XGBOOST) etc are applied and contrasted. The results
demonstrated that XGBoost outperformed the other algorithms, achieving an
accuracy of f 82%, precision of 88%, recall of 80%, and an F1-Score of 83%.