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Titlebook: Deployable Machine Learning for Security Defense; Second International Gang Wang,Arridhana Ciptadi,Ali Ahmadzadeh Conference proceedings 20

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書(shū)目名稱Deployable Machine Learning for Security Defense
副標(biāo)題Second International
編輯Gang Wang,Arridhana Ciptadi,Ali Ahmadzadeh
視頻videohttp://file.papertrans.cn/266/265762/265762.mp4
叢書(shū)名稱Communications in Computer and Information Science
圖書(shū)封面Titlebook: Deployable Machine Learning for Security Defense; Second International Gang Wang,Arridhana Ciptadi,Ali Ahmadzadeh Conference proceedings 20
描述This book constitutes selected and extended papers from the Second International Workshop on?Deployable Machine Learning for Security Defense, MLHat 2021, held in August 2021. Due to the COVID-19 pandemic the conference was held online.?.The 6 full papers were thoroughly reviewed and selected from 7 qualified submissions. The papers are organized in topical sections on machine learning for security, and malware attack and defense..
出版日期Conference proceedings 2021
關(guān)鍵詞computer crime; computer networks; computer science; computer security; computer systems; cryptography; da
版次1
doihttps://doi.org/10.1007/978-3-030-87839-9
isbn_softcover978-3-030-87838-2
isbn_ebook978-3-030-87839-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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: A Simple, yet Effective Deep Learning Approach to Android Malware Detection Based on Image Represee formats thus appear attractive to other fields such as malware detection, where deep learning on images alleviates the need for comprehensively hand-crafted features generalising to different malware variants. We postulate that this research direction could become the next frontier in Android malw
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Attacks on Visualization-Based Malware Detection: Balancing Effectiveness and Executabilitye detection. By converting binary code into images, researchers have shown satisfactory results in applying machine learning to extract features that are difficult to discover manually. Such visualization-based malware detection methods can capture malware patterns from many different malware famili
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A Survey on Common Threats in npm and PyPi Registriesveral frameworks to facilitate automation tasks further. Some of these frameworks are Node Manager Package (npm) and Python Package Index (PyPi), which are open source (OS) package libraries. The public registries npm and PyPi use to host packages allow any user with a verified email to publish code
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