(Top) An example of sub-sequence sampling from a raw byte sequence using a sliding-window technique with a window size of 4, and (bottom) an example of byte-level n-gram tokenization for the given sub-sequence .

(Top) An example of sub-sequence sampling from a raw byte sequence using a sliding-window technique with a window size of 4, and (bottom) an example of byte-level n-gram tokenization for the given sub-sequence .

<p>(Top) An example of sub-sequence sampling from a raw byte sequence using a sliding-window technique with a window size of 4, and (bottom) an example of byte-level n-gram tokenization for the given sub-sequence .</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
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

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