Intrusion Detection Using Deep Learning - Elmasry, Wisam
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Intrusion detection is deemed to be a cornerstone of cyber security. Early and effective intrusion detection has been attracted much attention from researchers in the last decade. However, the existence of a deep and adequate study in using deep learning models for intrusion detection in cyber security is still seldom. In this study, I have investigated the problem of intrusion detection in three different environments, namely, personal computer, network and cloud computing. Furthermore, a double Particle Swarm Optimization-based algorithm is proposed for both feature and hyperparameter…mehr

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Produktbeschreibung
Intrusion detection is deemed to be a cornerstone of cyber security. Early and effective intrusion detection has been attracted much attention from researchers in the last decade. However, the existence of a deep and adequate study in using deep learning models for intrusion detection in cyber security is still seldom. In this study, I have investigated the problem of intrusion detection in three different environments, namely, personal computer, network and cloud computing. Furthermore, a double Particle Swarm Optimization-based algorithm is proposed for both feature and hyperparameter selection. Finally, a novel deep learning approach is presented to improve the performance of intrusion detection in cyber security area.
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Autorenporträt
Wisam Elmasry received his B.Sc. and M.Sc. degrees in Computer Engineering from The Islamic University of Gaza (IUG), Palestine in 2004 and 2010, respectively. In 2019, he completed his doctorate in Computer Engineering at Istanbul Commerce University in Turkey. His areas of research interest include Cryptography, Cyber Security and Deep Learning.