An empirical study of problems and evaluation of IoT malware classification label sources

An empirical study of problems and evaluation of IoT malware classification label sources

With the proliferation of malware on IoT devices, research on IoT malicious code has also become more mature. Most studies use learning models to detect or classify malware. Therefore, ensuring high-quality labels for malware samples is crucial to maintaining research accuracy. Researchers typically...

書誌詳細
主要な著者:	Tianwei Lei, Jingfeng Xue, Yong Wang, Thar Baker, Zequn Niu
フォーマット:	論文
言語:	English
出版事項:	Elsevier 2024-01-01
シリーズ:	Journal of King Saud University: Computer and Information Sciences
主題:	Malware classification Malware label IoT malware analysis Anti-Virus engine label Label tool
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S1319157823004524

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