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Online encoder-decoder anomaly detection using encoder-decoder architecture with novel self-configuring neural networks & pure linear genetic programming for embedded systems

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Titel: Online encoder-decoder anomaly detection using encoder-decoder architecture with novel self-configuring neural networks & pure linear genetic programming for embedded systems
Autoren: Kasparaviciute, Gabriele, 1990, Thelin, Malin, Nordin, Peter, 1965, Söderstam, Per, Magnusson, Christian, Almljung, Mattias
Quelle: 11th International Joint Conference on Computational Intelligence, IJCCI 2019, Vienna, Austria IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence. :163-171
Schlagwörter: Neural network, Encoder-decoder, Evolutionary algorithm, Genetic algorithm, Embedded, Self-configuring, Anomaly detection, Linear genetic programming
Beschreibung: Recent anomaly detection techniques focus on the use of neural networks and an encoder-decoder architecture. However, these techniques lead to trade offs if implemented in an embedded environment such as high heat management, power consumption and hardware costs. This paper presents two related new methods for anomaly detection within data sets gathered from an autonomous mini-vehicle with a CAN bus. The first method which to the best of our knowledge is the first use of encoder-decoder architecture for anomaly detection using linear genetic programming (LGP). Second method uses self-configuring neural network that is created using evolutionary algorithm paradigm learning both architecture and weights suitable for embedded systems. Both approaches have the following advantages: it is inexpensive regarding resource use, can be run on almost any embedded board due to linear register machine advantages in computation. The proposed methods are also faster by at least one order of magnitude, and it includes both inference and complete training.
Dateibeschreibung: electronic
Zugangs-URL: https://research.chalmers.se/publication/519597
https://research.chalmers.se/publication/513654
https://research.chalmers.se/publication/519597/file/519597_Fulltext.pdf
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  Data: Online encoder-decoder anomaly detection using encoder-decoder architecture with novel self-configuring neural networks & pure linear genetic programming for embedded systems
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kasparaviciute%2C+Gabriele%22">Kasparaviciute, Gabriele</searchLink>, 1990<br /><searchLink fieldCode="AR" term="%22Thelin%2C+Malin%22">Thelin, Malin</searchLink><br /><searchLink fieldCode="AR" term="%22Nordin%2C+Peter%22">Nordin, Peter</searchLink>, 1965<br /><searchLink fieldCode="AR" term="%22Söderstam%2C+Per%22">Söderstam, Per</searchLink><br /><searchLink fieldCode="AR" term="%22Magnusson%2C+Christian%22">Magnusson, Christian</searchLink><br /><searchLink fieldCode="AR" term="%22Almljung%2C+Mattias%22">Almljung, Mattias</searchLink>
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  Label: Source
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  Data: <i>11th International Joint Conference on Computational Intelligence, IJCCI 2019, Vienna, Austria IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence</i>. :163-171
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Neural+network%22">Neural network</searchLink><br /><searchLink fieldCode="DE" term="%22Encoder-decoder%22">Encoder-decoder</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithm%22">Evolutionary algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithm%22">Genetic algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Embedded%22">Embedded</searchLink><br /><searchLink fieldCode="DE" term="%22Self-configuring%22">Self-configuring</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection%22">Anomaly detection</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+genetic+programming%22">Linear genetic programming</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Recent anomaly detection techniques focus on the use of neural networks and an encoder-decoder architecture. However, these techniques lead to trade offs if implemented in an embedded environment such as high heat management, power consumption and hardware costs. This paper presents two related new methods for anomaly detection within data sets gathered from an autonomous mini-vehicle with a CAN bus. The first method which to the best of our knowledge is the first use of encoder-decoder architecture for anomaly detection using linear genetic programming (LGP). Second method uses self-configuring neural network that is created using evolutionary algorithm paradigm learning both architecture and weights suitable for embedded systems. Both approaches have the following advantages: it is inexpensive regarding resource use, can be run on almost any embedded board due to linear register machine advantages in computation. The proposed methods are also faster by at least one order of magnitude, and it includes both inference and complete training.
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  Data: electronic
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.5220/0008064401630171
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 163
    Subjects:
      – SubjectFull: Neural network
        Type: general
      – SubjectFull: Encoder-decoder
        Type: general
      – SubjectFull: Evolutionary algorithm
        Type: general
      – SubjectFull: Genetic algorithm
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      – SubjectFull: Embedded
        Type: general
      – SubjectFull: Self-configuring
        Type: general
      – SubjectFull: Anomaly detection
        Type: general
      – SubjectFull: Linear genetic programming
        Type: general
    Titles:
      – TitleFull: Online encoder-decoder anomaly detection using encoder-decoder architecture with novel self-configuring neural networks & pure linear genetic programming for embedded systems
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            NameFull: Kasparaviciute, Gabriele
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            NameFull: Thelin, Malin
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            NameFull: Nordin, Peter
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            NameFull: Söderstam, Per
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            NameFull: Magnusson, Christian
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            NameFull: Almljung, Mattias
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2019
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            – TitleFull: 11th International Joint Conference on Computational Intelligence, IJCCI 2019, Vienna, Austria IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence
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