Why GloVe Shows Negative Effects in Malware Classification


연구 분야: Safety



학회: 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)


초록

The past decades witness the development of various Machine Learning (ML) models for malware classification. Semantic representation is a crucial basis for these classifiers. This paper aims to assess the effect of semantic representation methods on malware classifier performance. Two commonly-used semantic representation methods including N-gram and GloVe. We utilize diverse ML classifiers to conduct comparative experiments to analyze the capability of N-gram, GloVe and image-based methods for malware classification. We also analyze deeply the reason why the GloVe can produce negative effects on malware static analysis.


Author Profile
Bingchu Jin

Economics & Technology Research Institute State Grid Shanxi Electric Power Company Taiyuan China

China
Author Profile
Zesheng Hu

Economics & Technology Research Institute State Grid Shanxi Electric Power Company Taiyuan China

China
Author Profile
Jianhua Wang

Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China

Andorra

📄 논문 정보

발행 연도 2022년
인용수 37
출판 국가 Andorra, China
사이트 IEEE
좋아요 수 0

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