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

Gabrielė Kasparavičiūtė, Malin Thelin, Peter Nordin, Per Söderstam, Christian Magnusson, Mattias Almljung


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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