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Titlebook: Machine Intelligence and Big Data Analytics for Cybersecurity Applications; Yassine Maleh,Mohammad Shojafar,Youssef Baddi Book 2021 The Ed

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书目名称Machine Intelligence and Big Data Analytics for Cybersecurity Applications
编辑Yassine Maleh,Mohammad Shojafar,Youssef Baddi
视频video
概述Presents the latest discoveries in terms of machine intelligence and Big data analytics techniques and methods for cybersecurity and privacy.Proposes many case studies and applications of machine inte
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Machine Intelligence and Big Data Analytics for Cybersecurity Applications;  Yassine Maleh,Mohammad Shojafar,Youssef Baddi Book 2021 The Ed
描述.This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field. Bridging an important gap between machine intelligence, big data, and cybersecurity communities, it aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploringthe latest advances on machine intelligence and big data analytics for cybersecurity applications... .. .
出版日期Book 2021
关键词Cybersecurity; Machine Intelligence; Big Data; Forensics; Cybercrime; Deep Learning; Machine Learning; Cybe
版次1
doihttps://doi.org/10.1007/978-3-030-57024-8
isbn_softcover978-3-030-57026-2
isbn_ebook978-3-030-57024-8Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Improving Cyber-Threat Detection by Moving the Boundary Around the Normal Samplesdetection models in various scenarios. However, it often suffers from training data over-fitting. In this paper, we propose a supervised machine learning method for cyber-threat detection, which modifies the training set to reduce data over-fitting when training a deep neural network. This is done b
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IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Network the field of cybersecurity. Many researchers have already proposed several anti-phishing approaches to detect phishing in terms of email, webpages, images, or links. This study also aimed to propose and implement an intelligent framework to detect phishing URLs (Uniform Resource Locator). It has be
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