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Titlebook: Cyber Malware; Offensive and Defens Iman Almomani,Leandros A. Maglaras,Nick Ayres Book 2024 The Editor(s) (if applicable) and The Author(s)

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發(fā)表于 2025-3-21 17:51:31 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Cyber Malware
副標題Offensive and Defens
編輯Iman Almomani,Leandros A. Maglaras,Nick Ayres
視頻videohttp://file.papertrans.cn/242/241718/241718.mp4
概述Presents theoretical, technical, and practical knowledge on defending against malware attacks.Covers malware applications using machine learning algorithms, Blockchain and AI, forensics tools, and muc
叢書名稱Security Informatics and Law Enforcement
圖書封面Titlebook: Cyber Malware; Offensive and Defens Iman Almomani,Leandros A. Maglaras,Nick Ayres Book 2024 The Editor(s) (if applicable) and The Author(s)
描述This book provides the foundational aspects of malware attack vectors and appropriate defense mechanisms against malware. The book equips readers with the necessary knowledge and techniques to successfully lower the risk against emergent malware attacks. Topics cover protections against malware using machine learning algorithms, Blockchain and AI technologies, smart AI-based applications, automated detection-based AI tools, forensics tools, and much more.?The authors discuss theoretical, technical, and practical issues related to cyber malware attacks and defense, making it ideal reading material for students, researchers, and developers..
出版日期Book 2024
關鍵詞Malware analysis; malware forensics; data mining; anti-malware; malware detection; malware distribution; m
版次1
doihttps://doi.org/10.1007/978-3-031-34969-0
isbn_softcover978-3-031-34971-3
isbn_ebook978-3-031-34969-0Series ISSN 2523-8507 Series E-ISSN 2523-8515
issn_series 2523-8507
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書目名稱Cyber Malware影響因子(影響力)




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書目名稱Cyber Malware網(wǎng)絡公開度學科排名




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書目名稱Cyber Malware被引頻次學科排名




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沙發(fā)
發(fā)表于 2025-3-21 22:20:27 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:59:12 | 只看該作者
A Princely Pandect on Astronomyng issue due to their simple yet powerful concealment of malicious network services. This book chapter comprehensively discusses FFSNs, focusing on fast-flux architecture, operation, and characterization. Also, it provides a review of fast-flux detection mechanisms and highlights the main challenges and future research directions.
地板
發(fā)表于 2025-3-22 05:58:57 | 只看該作者
Fast-Flux Service Networks: Architecture, Characteristics, and Detection Mechanisms,ng issue due to their simple yet powerful concealment of malicious network services. This book chapter comprehensively discusses FFSNs, focusing on fast-flux architecture, operation, and characterization. Also, it provides a review of fast-flux detection mechanisms and highlights the main challenges and future research directions.
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A Deep-Vision-Based Multi-class Classification System of Android Malware Apps, must be used to differentiate between benign and malware Android apps. Unfortunately, conventional malware detection and classification techniques based on traditional static- or dynamic-based machine learning (ML) algorithms are not the best choices for malware analysis applications. These traditi
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發(fā)表于 2025-3-22 21:37:22 | 只看該作者
Android Malware Detection Based on Network Analysis and Federated Learning,cation, becoming a serious threat to network security and user privacy. In addition, with a large-scale Android system deployment and the raising of privacy concerns, data heterogeneity, availability, and privacy preservation are presenting major challenges when applying traditional cloud-based and
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Fast-Flux Service Networks: Architecture, Characteristics, and Detection Mechanisms,highly resilient service for their malicious servers while remaining hidden from direct access. This is achieved by configuring many botnet machines to work as proxies that relay traffic between end users and malicious servers controlled by botherders. FFSNs are becoming popular for hosting maliciou
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