Using Sequence Analysis to Perform Application-Based Anomaly Detection within an Artificial Immune System Framework (Paperback)


The Air Force and other Department of Defense (DoD) computer systems typically rely on traditional signature-based network IDSs to detect various types of attempted or successful attacks. Signature-based methods are limited to detecting known attacks or similar variants; anomaly-based systems, by contrast, alert on behaviors previously unseen. The development of an effective anomaly-detecting, application-based IDS would increase the Air Force's ability to ward off attacks that are not detected by signature-based network IDSs, thus strengthening the layered defenses necessary to acquire and maintain safe, secure communication capability. This system follows the Artificial Immune System (AIS) framework, which relies on a sense of "self," or normal system states to determine potentially dangerous abnormalities ("non-self").

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Product Description

The Air Force and other Department of Defense (DoD) computer systems typically rely on traditional signature-based network IDSs to detect various types of attempted or successful attacks. Signature-based methods are limited to detecting known attacks or similar variants; anomaly-based systems, by contrast, alert on behaviors previously unseen. The development of an effective anomaly-detecting, application-based IDS would increase the Air Force's ability to ward off attacks that are not detected by signature-based network IDSs, thus strengthening the layered defenses necessary to acquire and maintain safe, secure communication capability. This system follows the Artificial Immune System (AIS) framework, which relies on a sense of "self," or normal system states to determine potentially dangerous abnormalities ("non-self").

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Product Details

General

Imprint

Biblioscholar

Country of origin

United States

Release date

October 2012

Availability

Expected to ship within 10 - 15 working days

First published

October 2012

Authors

Dimensions

246 x 189 x 5mm (L x W x T)

Format

Paperback - Trade

Pages

100

ISBN-13

978-1-249-83309-3

Barcode

9781249833093

Categories

LSN

1-249-83309-4



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