Mobile hybrid intrusion detection : the MOVICAB-IDS system /

This monograph comprises work on network-based Intrusion Detection (ID) that is grounded in visualisation and hybrid Artificial Intelligence (AI). It has led to the design of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a novel Intrusion Detection System (IDS), which is comprehe...

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Bibliographic Details
Author / Creator:Herrero, Alvaro.
Imprint:Berlin ; Heidelberg : Springer, ©2011.
Description:1 online resource (vi, 146 pages) : illustrations.
Language:English
Series:Studies in computational intelligence ; v. 334
Studies in computational intelligence ; v. 334.
Subject:Intrusion detection systems (Computer security)
Artificial intelligence.
Artificial intelligence.
Intrusion detection systems (Computer security)
Ingénierie.
Artificial intelligence.
Intrusion detection systems (Computer security)
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11075317
Hidden Bibliographic Details
Other authors / contributors:Corchado, Emilio.
ISBN:9783642182990
3642182992
9783642182983
Notes:Includes bibliographical references (pages 129-146).
Print version record.
Summary:This monograph comprises work on network-based Intrusion Detection (ID) that is grounded in visualisation and hybrid Artificial Intelligence (AI). It has led to the design of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a novel Intrusion Detection System (IDS), which is comprehensively described in this book. This novel IDS combines different AI paradigms to visualise network traffic for ID at packet level. It is based on a dynamic Multiagent System (MAS), which integrates an unsupervised neural projection model and the Case-Based Reasoning (CBR) paradigm through the use of deliberative agents that are capable of learning and evolving with the environment. The proposed novel hybrid IDS provides security personnel with a synthetic, intuitive snapshot of network traffic and protocol interactions. This visualisation interface supports the straightforward detection of anomalous situations and their subsequent identification. The performance of MOVICAB-IDS was tested through a novel mutation-based testing method in different real domains which entailed several attacks and anomalous situations.