Malfustection: Obfuscated Malware Detection and Malware Classification with Data Shortage by Combining Semi-Supervised and Contrastive Learning | ||
| Journal of Innovations in Computer Science and Engineering (JICSE) | ||
| دوره 3، شماره 1، شهریور 2025، صفحه 1-21 اصل مقاله (1.9 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.48308/jicse.2025.237344.1041 | ||
| نویسندگان | ||
| Mohammad Mahdi Maghouli1؛ Mohamadreza Fereydooni2؛ Mojtaba Vahidi Asl2؛ Monireh Abdoos* 3 | ||
| 1Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran. | ||
| 2Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran | ||
| 3Shahid Beheshti University Faculty of Computer Science and Engineering artificial intelligence | ||
| چکیده | ||
| With the advent of new technologies, using various formats of digital gadgets is becoming widespread. In today's world, where everyday tasks are inevitable without technology, this extensive use of computers paves the way for malicious activity. Malware is one of the well-known and widely used means utilized for doing destructive activities by malicious attackers. Producing malware from scratch is somewhat difficult, so attackers tend to obfuscate existing malware and prepare it to become an unrecognizable program. Since creating new malware from an old one using obfuscation is a creative task, there are some drawbacks to identifying obfuscated malwares. In this research, we propose a solution to overcome this problem by converting the code to an image in the first step and then using a semi-supervised approach combined with contrastive learning. In this case, an obfuscation in the malware bytecode corresponds to an augmentation in the image. Hence, by utilizing meaningful augmentations, which simulate some obfuscation changes and combine them to generate complex ambiguity procedures, our proposed solution is able to construct, learn, and detect a wide range of obfuscations. This work addresses two issues: 1) malware classification despite the data deficiency and 2) obfuscated malware detection by training on non-obfuscated malwares. According to the results, the proposed method overcomes the data shortage problem in malware classification, as its accuracy is 90.1% when just 10% of data is used for training the model. Moreover, training on basic malwares without obfuscation achieved 96.21 percent accuracy in detecting obfuscated malware. | ||
| کلیدواژهها | ||
| Malware Classification؛ obfuscated malware detection؛ semi-supervised learning؛ contrastive learning؛ code to image transformation | ||
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آمار تعداد مشاهده مقاله: 186 تعداد دریافت فایل اصل مقاله: 230 |
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