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Procreating V-detectors for nonself recognition: an application to anomaly detection in power systems

Published: 07 July 2007 Publication History

Abstract

The artificial immune system approach for self-nonself discrimination and its application to anomaly detection problems in engineering is showing great promise. A seminal contribution in this area is the V-detectors algorithm that can very effectively cover the nonself region of the feature space with a set of detectors. The detector set can be used to detect anomalous inputs. In this paper, a multistage approach to create an effective set of V-detectors is considered. The first stage of the algorithm generates an initial set of V-detectors. In subsequent stage, new detectors are grown from existing ones, by means of a mechanism called procreation. Procreating detectors can more effectively fill hard-to-reach interstices in the nonself region, resulting in better coverage. The effectiveness of the algorithm is first illustrated by applying it to a well-known fractal, the Koch curve. The algorithm is then applied to the problem of detecting anomalous behavior in power distribution systems, and can be of much use for maintenance-related decision-making in electrical utility companies.

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Cited By

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  • (2009)A New Algorithm Based on Negative Selection and Idiotypic Networks for Generating Parsimonious Detector Sets for Industrial Fault Detection ApplicationsProceedings of the 8th International Conference on Artificial Immune Systems10.1007/978-3-642-03246-2_27(288-300)Online publication date: 12-Aug-2009
  • (2008)Artificial Immune Systems for Self-Nonself Discrimination: Application to Anomaly DetectionAdvances of Computational Intelligence in Industrial Systems10.1007/978-3-540-78297-1_11(231-248)Online publication date: 2008
  • (2007)Anomaly Detection in Animal-Related Failures in Overhead Distribution Systems2007 39th North American Power Symposium10.1109/NAPS.2007.4402356(498-504)Online publication date: Sep-2007

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  1. Procreating V-detectors for nonself recognition: an application to anomaly detection in power systems

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        cover image ACM Conferences
        GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
        July 2007
        2313 pages
        ISBN:9781595936974
        DOI:10.1145/1276958
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 07 July 2007

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        Author Tags

        1. anomaly detection
        2. artificial immune systems
        3. immunocomputing

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        GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
        Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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        • (2009)A New Algorithm Based on Negative Selection and Idiotypic Networks for Generating Parsimonious Detector Sets for Industrial Fault Detection ApplicationsProceedings of the 8th International Conference on Artificial Immune Systems10.1007/978-3-642-03246-2_27(288-300)Online publication date: 12-Aug-2009
        • (2008)Artificial Immune Systems for Self-Nonself Discrimination: Application to Anomaly DetectionAdvances of Computational Intelligence in Industrial Systems10.1007/978-3-540-78297-1_11(231-248)Online publication date: 2008
        • (2007)Anomaly Detection in Animal-Related Failures in Overhead Distribution Systems2007 39th North American Power Symposium10.1109/NAPS.2007.4402356(498-504)Online publication date: Sep-2007

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