Averaged precision, recall, and F1 scores of ‘malware’ class on HWP format with different pre-training strategies, where the three boxes of each pre-training combination indicate precision, recall, and F1 scores, respectively.

Averaged precision, recall, and F1 scores of ‘malware’ class on HWP format with different pre-training strategies, where the three boxes of each pre-training combination indicate precision, recall, and F1 scores, respectively.

<p>Averaged precision, recall, and F1 scores of ‘malware’ class on HWP format with different pre-training strategies, where the three boxes of each pre-training combination indicate precision, recall, and F1 scores, respectively.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Eun-Jin Kim (305808) (author)
مؤلفون آخرون:	Yun-Kyung Lee (10865965) (author), Sang-Min Lee (474684) (author), Jeong-Nyeo Kim (22421623) (author), Ah Reum Kang (8720862) (author), Mi-seo Kim (22421626) (author), Young-Seob Jeong (12800186) (author)
منشور في:	2025
الموضوعات:	Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified three file types network every day making neural network empirical results demonstrate capture local patterns natural language processing masked language modeling detect malicious activities based language models malware detection task malware detection services processes byte sequences p &# 8764 including conventional pre based language model trained transformer encoder malware detection language model byte sequences based model powerful encoder malicious attacks foundation model speech recognition sequence prediction sentence prediction recent studies possible combinations novel transformer news articles ms office image analysis fake prediction benchmark consisting approximately 2
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