| 期刊全称 | Android Malware Detection using Machine Learning | | 期刊简称 | Data-Driven Fingerpr | | 影响因子2023 | ElMouatez Billah Karbab,Mourad Debbabi,Djedjiga Mo | | 视频video | | | 发行地址 | Presents android malware detection framework using machine learning techniques, as well as static and dynamic analysis features.Introduces fingerprinting and clustering system of android malware using | | 学科分类 | Advances in Information Security | | 图书封面 |  | | 影响因子 | The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures..First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Basedon this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malic | | Pindex | Book 2021 |
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发表于 2025-3-21 18:11:55
