Intrusion Detection: A Data Mining Approach

Intrusion Detection: A Data Mining Approach

English | ISBN: 9811527156 | 2020 | 136 Pages | PDF | 5 MB

This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion.
The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.

Download:

http://longfiles.com/elhq9e6sel4w/Intrusion_Detection_A_Data_Mining_Approach.pdf.html

[Fast Download] Intrusion Detection: A Data Mining Approach


Related eBooks:
About Face 3: The Essentials of Interaction Design
Deep Learning-Based Approaches for Sentiment Analysis
Dynamic Software Development
Mobile Service Computing
Beyond the Horizon of Computability
Clojure Data Analysis Cookbook - Second Edition
Interactive Multimedia
Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes
Second Handbook of Information Technology in Primary and Secondary Education
Advances in Multidisciplinary Retrieval
Pragmatic Guide to Subversion
Glow Kids: How Screen Addiction Is Hijacking Our Kids-and How to Break the Trance
Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.