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Titlebook: Machine Learning for Cybersecurity; Innovative Deep Lear Marwan Omar Book 2022 The Author(s), under exclusive license to Springer Nature Sw

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发表于 2025-3-21 19:29:29 | 显示全部楼层 |阅读模式
书目名称Machine Learning for Cybersecurity
副标题Innovative Deep Lear
编辑Marwan Omar
视频video
概述Learn emerging machine learning techniques to manage data and defend your information system networks using the Python ecosystem.Apply Deep Learning to malware anomaly detection, intrusion detection s
丛书名称SpringerBriefs in Computer Science
图书封面Titlebook: Machine Learning for Cybersecurity; Innovative Deep Lear Marwan Omar Book 2022 The Author(s), under exclusive license to Springer Nature Sw
描述This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry..By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. .The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effective.Advanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this Sp
出版日期Book 2022
关键词machine learning; Cybersecurity; deep learning; malware detection; anomaly detection; Cyber attacks; decis
版次1
doihttps://doi.org/10.1007/978-3-031-15893-3
isbn_softcover978-3-031-15892-6
isbn_ebook978-3-031-15893-3Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s), under exclusive license to Springer Nature Switzerland AG 2022
The information of publication is updating

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发表于 2025-3-21 23:29:23 | 显示全部楼层
New Approach to Malware Detection Using Optimized Convolutional Neural Network, and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and impleme
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发表于 2025-3-22 08:11:01 | 显示全部楼层
Book 2022olve certain challenges facing the cybersecurity industry..By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. .The knowledge and tools introduc
发表于 2025-3-22 11:18:20 | 显示全部楼层
Malware Anomaly Detection Using Local Outlier Factor Technique,ectiveness of our technique on real-world datasets. This is an efficient technique for malware detection as the model trained for this purpose is based on unsupervised learning. The model trains on the anomalies, that is, the unusual behavior in a process, making it significantly effective.
发表于 2025-3-22 15:33:54 | 显示全部楼层
Application of Machine Learning (ML) to Address Cybersecurity Threats,various problem domains in cybersecurity. To achieve this objective, a rapid evidence assessment (REA) of existing scholarly literature on the subject matter is adopted. The aim is to present a snapshot of the various ways ML is being applied to help address cybersecurity threat challenges.
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发表于 2025-3-23 00:00:35 | 显示全部楼层
Application of Machine Learning (ML) to Address Cybersecurity Threats,s has prompted the use of machine learning (hereafter, ML) to help address the problem. But as organizations increasingly use intelligent cybersecurity techniques, the overall efficacy and benefit analysis of these ML-based digital security systems remain a subject of increasing scholarly inquiry. T
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