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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Malware Analysis for IoT and Smart AI-Based Applications, require a reliable and secure communication structure. The enormous growth in wireless communication and the development of 5G brings an opportunity for many emerging IoT and smart AI-based applications. These networks have increased traffic and increased the level of complexity. It brings many new

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Modeling for Prey-Predator Relation different DL algorithms for malware detection and recognition. The vision-based classification system was evaluated comprehensively using two open-source Android datasets (CICAndMal2017 and CICMalDroid2020). The binary formats of the android apps included in these datasets were first converted into

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A Primer on Population Dynamics Modelingfectively while maintaining data privacy. Specifically, we trained a convolutional neural network model using FL-based decentralized optimization for detecting Android malware based on network behavior. Then, we evaluated the proposed training methodology with the benchmark dataset AAGM-2017 under d

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Book Four. On Distances and Bodies We provide a detailed taxonomy that classifies these solutions according to various criteria including the analysis task, the nature of the extracted features, the used features representation method, and the used deep learning algorithms. Furthermore, we discuss these solutions with respect to the