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Titlebook: Malware Analysis Using Artificial Intelligence and Deep Learning; Mark Stamp,Mamoun Alazab,Andrii Shalaginov Book 2021 The Editor(s) (if a

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发表于 2025-3-21 16:36:38 | 显示全部楼层 |阅读模式
书目名称Malware Analysis Using Artificial Intelligence and Deep Learning
编辑Mark Stamp,Mamoun Alazab,Andrii Shalaginov
视频video
概述Explores how deep learning and artificial intelligence can effectively be used in malware detection and analysis.Showcases state-of-the-art tools, frameworks and techniques to enable readers to implem
图书封面Titlebook: Malware Analysis Using Artificial Intelligence and Deep Learning;  Mark Stamp,Mamoun Alazab,Andrii Shalaginov Book 2021 The Editor(s) (if a
描述.​This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed..This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases..
出版日期Book 2021
关键词Malware identification and analysis; Intrusion detection; Computer forensics; Spam detection; Phishing d
版次1
doihttps://doi.org/10.1007/978-3-030-62582-5
isbn_softcover978-3-030-62584-9
isbn_ebook978-3-030-62582-5
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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发表于 2025-3-21 20:26:24 | 显示全部楼层
Malware Detection with Sequence-Based Machine Learning and Deep Learningatatypes extracted from code: static features and dynamic traces of program execution. We review recent research that applies machine learning on opcode and API call sequences, call graphs, system calls, registry changes, information flow traces, as well as hybrid and raw data, to detect and classif
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A Survey of Intelligent Techniques for Android Malware Detectionted with the network and provide the online functionality and services available with the lowest cost. In this context, the Android operating system (OS) is very popular due to its openness. It has major stakeholder in the smart devices but has also become an attractive target for cyber-criminals. T
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Review of Artificial Intelligence Cyber Threat Assessment Techniques for Increased System Survivabilrs of such systems. The notion of survivability in the context of cybersecurity over multi-user distributed information systems is defined, which is set as the target of cyber defense to prevent the adversary from successfully completing their mission. The cyber-attackers’ kill chain is explained. A
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