Online Encoder-decoder Anomaly Detection using Encoder-decoder Architecture with Novel Self-configuring Neural Networks \& Pure Linear Genetic Programming for Embedded Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{DBLP:conf/ijcci/KasparaviciuteT19,

• title = "Online Encoder-decoder Anomaly Detection using Encoder-decoder Architecture with Novel Self-configuring Neural Networks {\&} Pure Linear Genetic Programming for Embedded Systems",
• author = "Gabriele Kasparaviciute and Malin Thelin and Peter Nordin and Per Soderstam and Christian Magnusson and Mattias Almljung",
• booktitle = "Proceedings of the 11th International Joint Conference on Computational Intelligence, IJCCI 2019",
• editor = "Juan Julian Merelo Guervos and Jonathan M. Garibaldi and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick",
• year = "2019",
• pages = "163--171",
• publisher = "ScitePress",
• month = sep # " 17-19",
• address = "Vienna, Austria",
• keywords = "genetic algorithms, genetic programming, Linear Genetic Programming, ANN, Encoder-decoder, Anomaly Detection, Evolutionary Algorithm, Embedded, Self-configuring, Neural Network, Evolutionary Learning Systems, Evolvable Computing, Artificial Intelligence, Computational Intelligence, Informatics in Control, Automation and Robotics, Intelligent Control Systems and Optimization, Soft Computing",
• isbn13 = "978-989-758-384-1",
• URL = "https://doi.org/10.5220/0008064401630171",
• DOI = "doi:10.5220/0008064401630171",
• timestamp = "Mon, 27 Apr 2020 13:47:06 +0200",
• biburl = "https://dblp.org/rec/conf/ijcci/KasparaviciuteT19.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• abstract = "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.",

}

Genetic Programming entries for Gabriele Kasparaviciute Malin Thelin Peter Nordin Per Soderstam Christian Magnusson Mattias Almljung

Citations