https://scholars.lib.ntu.edu.tw/handle/123456789/607464
標題: | Toward an Effective Black-Box Adversarial Attack on Functional JavaScript Malware against Commercial Anti-Virus | 作者: | Tsai Y.-D Chen C SHOU-DE LIN |
關鍵字: | adversarial attack;malware detection;neural networks;High level languages;Machine learning;Viruses;Adversarial attack;Attack methods;Black boxes;Classification algorithm;Input noise;Javascript;Javascript malware;Malware detection;Neural-networks;Computer viruses | 公開日期: | 2021 | 起(迄)頁: | 4165-4172 | 來源出版物: | International Conference on Information and Knowledge Management, Proceedings | 摘要: | Machine learning has been a rising technique in signatureless malware detection and is popular in the anti-virus industry. Despite the powerful ability of machine learning, it is known to be vulnerable to attack by injecting specially crafted input noise (adversarial example). In this paper, we develop a systematic attack method that is effective, general and also efficient which automatically generates functional malware. Experiment results showed that such adversarial malware could deceive commercial anti-virus and completely defeat learning-based malware detector provided by a well-known anti-virus vendor. We further examine the effectiveness of our approach on multiple anti-virus engines on VirusTotal and investigate the transferability of our proposed method between different features and classification algorithms. Finally, we show how our attack could resist JavaScript de-obfuscation techniques. ? 2021 ACM. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119191307&doi=10.1145%2f3459637.3481956&partnerID=40&md5=761e29a9e6a517855321cf26b3336bce https://scholars.lib.ntu.edu.tw/handle/123456789/607464 |
DOI: | 10.1145/3459637.3481956 |
顯示於: | 資訊工程學系 |
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