Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Network Intrusion Detection System using Machine Learning Techniques

Network Intrusion Detection System using Machine Learning Techniques

Sindhu, Siva S. Sivatha
Geetha, S.
Selvakumar, S.

Paperback

Networking

ISBN10: 3659410357
ISBN13: 9783659410352
Publisher: Lap Lambert Academic Pub
Published: Jun 28 2013
Pages: 80
Weight: 0.28
Height: 0.19 Width: 6.00 Depth: 9.00
Language: English
This book presents the need for intrusion detection system as it has become an essential concern with the growing use of internet and increased network attacks such as virus, Trojan horse, worms and creative hackers. In addition, the basic details about the historic origin of IDS, the types of IDS, their deployment schemes and general architecture are considered. IDS using various machine learning techniques like fuzzy logic, genetic algorithm, neural network, decision tree etc are discussed and their pros and cons are discussed. Another potential approach is ensemble learning, which have been successfully applied to IDS for differentiating normal and anomalous types. In this book, various ensemble approaches like neuro-genetic, neuro-fuzzy, neurotree etc are explained. The implementation of these IDS depends again on the requirement of the security administrator. The IDS discussed in this book are adaptive to new environments by updating the audit data with recent attacks. If new attacks are identified these approaches can store the attack patterns in log generator for detecting future attacks.

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