Almaguer-Angeles, Fernando

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Almaguer-Angeles, Fernando
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Almaguer-Angeles, Fernando

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  • Publication
    A framework for machine learning based anomaly detection in Internet of Things data analysis
    (University College Dublin. School of Computer Science, 2020)
    Almaguer-Angeles, Fernando 
    Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. Machine Learning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, I compare the classification performance of 22 state-of-the-art supervised ML classification algorithms on different IoT smart-buildings datasets, when applied to the problem of anomaly detection. The results show that there is a set of ML algorithms that fits almost perfectly a type of datasets. Based on these results and a characterization of our datasets, this thesis propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.
      396
  • Publication
    Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios
    (IEEE, 2019-04-18)
    Almaguer-Angeles, Fernando 
    ;
    Murphy, John 
    ;
    Murphy, Liam, B.E. 
    ;
    Portillo Dominguez, Andres Omar 
    Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. Machine Learning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, we compare the performance of 22 state-of-the-art supervised ML classification algorithms on different IoT datasets, when applied to the problem of anomaly detection. Our results show that there is no dominant solution, and that for each scenario, several candidate techniques perform similarly. Based on our results and a characterization of our datasets, we propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.
      498Scopus© Citations 6