Evaluation of N-Gram Based Multi-Layer Approach to Detect Malware in Android
作者： Takia Islam , Sheikh Shah Mohammad Motiur Rahman , Md Aumit Hasan , Abu Sayed Md Mostafizur Rahaman , Md Ismail Jabiullah
DOI: 10.1016/J.PROCS.2020.04.115
关键词:
摘要: Abstract N-gram techniques usually used in Natural Language Processing (NLP). Those along with stacked generalization has been experimented and assessed the field of android malware detection. Beacuse rapidly growing users, become most popular among attackers. Android gigantic topics information security. Various security researchers have already started to propose intelligency based In this paper, a details investigation performed evaluate effectiveness unigram, bigram trigram generalization. It’s found that stacking, unigram provides more than 97% accuracy which is highest detection rate against trigram. level 1, Extra Tree (ET), Random Forest (RF) Gradient Boosting (GB) are used. As final predictor meta estimator eXtreme (XGBoost) A strong basement use n-gram developing determined from study.
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