Intrusion Detection

Gunavathi Ramasamy

ISBN 10: 6206158853 / ISBN 13: 9786206158851

Published by LAP LAMBERT Academic Publishing Apr 2023, 2023

About this Item

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.

Seller Inventory # 9786206158851

Bibliographic Details

• Title: Intrusion Detection
• Publisher: LAP LAMBERT Academic Publishing Apr 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 6206158853
• ISBN 13: 9786206158851
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 15D millimeters 220 millimeters width by 150 millimeters height by 15 millimeters depth
• Weight: 369 grams

About this title

Synopsis

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.