Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset

• URL: http://arxiv.org/abs/2310.10670v1
• Date: Sun, 24 Sep 2023 16:28:35 GMT
• Title: Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset
• Authors: Suleman Qamar
• Abstract summary: This dataset comprises 40 distinct malware families having 21924 samples. It incorporates obfuscation techniques that mimic the strategies employed by malware creators. The purpose of this dataset is to provide a more realistic and representative environment for evaluating the effectiveness of malware analysis techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

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