Network intrusion detection is one of the central systems used in cyber security to prevent the intrusions in the organisation's networks. Tackling the attempts to compromise the confidentiality, integrity and availability of computer networks' security mechanisms in a big data environment is the most challenging task due to the volume and variety of big data. This study presented to tackle the challenges in network Intrusion Detection Systems (IDS) and demonstrate intelligent algorithms' development to detect the intrusions in big network data. The problem is the practical selection of the features from the network dataset as it dramatically impacts the intrusion detection accuracy. Hence, an efficient feature selection approach must be introduced to achieve higher accuracy with a reduced number of features.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Network intrusion detection is one of the central systems used in cyber security to prevent the intrusions in the organisation's networks. Tackling the attempts to compromise the confidentiality, integrity and availability of computer networks' security mechanisms in a big data environment is the most challenging task due to the volume and variety of big data. This study presented to tackle the challenges in network Intrusion Detection Systems (IDS) and demonstrate intelligent algorithms' development to detect the intrusions in big network data. The problem is the practical selection of the features from the network dataset as it dramatically impacts the intrusion detection accuracy. Hence, an efficient feature selection approach must be introduced to achieve higher accuracy with a reduced number of features. 236 pp. Englisch. N° de réf. du vendeur 9786206158851
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ramasamy GunavathiDr. R. Gunavathi is working as Associate Professor, CHRIST University. Her current research interest is in Mobile Ad Hoc Networks, Vehicular Ad hoc Networks, Data Analytics and Bigdata Analytics. She has produced 3. N° de réf. du vendeur 872703939
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Taschenbuch. Etat : Neu. Intrusion Detection | Map Reduce Based Deep Learning In Big Data Environment | Gunavathi Ramasamy (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206158851 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 126915335
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Network intrusion detection is one of the central systems used in cyber security to prevent the intrusions in the organisation's networks. Tackling the attempts to compromise the confidentiality, integrity and availability of computer networks' security mechanisms in a big data environment is the most challenging task due to the volume and variety of big data. This study presented to tackle the challenges in network Intrusion Detection Systems (IDS) and demonstrate intelligent algorithms' development to detect the intrusions in big network data. The problem is the practical selection of the features from the network dataset as it dramatically impacts the intrusion detection accuracy. Hence, an efficient feature selection approach must be introduced to achieve higher accuracy with a reduced number of features.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 236 pp. Englisch. N° de réf. du vendeur 9786206158851
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Network intrusion detection is one of the central systems used in cyber security to prevent the intrusions in the organisation's networks. Tackling the attempts to compromise the confidentiality, integrity and availability of computer networks' security mechanisms in a big data environment is the most challenging task due to the volume and variety of big data. This study presented to tackle the challenges in network Intrusion Detection Systems (IDS) and demonstrate intelligent algorithms' development to detect the intrusions in big network data. The problem is the practical selection of the features from the network dataset as it dramatically impacts the intrusion detection accuracy. Hence, an efficient feature selection approach must be introduced to achieve higher accuracy with a reduced number of features. N° de réf. du vendeur 9786206158851
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