Publicação
Time series classification for device fingerprinting: internship project at a telecommunications and technology company
| Resumo: | The telecommunication service providers seek an accurate insight into the devices that are connected within a home network, in order to provide a better in-home experience. In this way, the goal of the internship was to develop a machine learning model for fingerprinting of Amazon devices. This can be translated to a timeseries binary classification problem and assumes an exploration background of understanding the employment of bytes received by the router over time as an indicator of the internet usage to detect the Amazon devices. A feature-based analysis was conducted to make it possible to apply the most common and simple classifiers, which is relevant within a company context. The available data presented some challenges, namely a high imbalance and number of missing values. For this, it was studied several combinations of different techniques to increase the importance of the minority class and to impute the unknown values. In addition, multiple models were trained, whose results were evaluated and compared. The achieved performance of the best model was not considered satisfactory to correctly identify the Amazon devices, which lead to the conclusion that other approaches, algorithms and/or variable(s) need to be considered in a future iteration. The project contributed to a better understanding of the path to take on the identification of the devices and introduced new approaches and reasoning when dealing with similar data as the timeseries in analysis. |
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| Autores principais: | Alves, Ana Sofia Tavares Jordão |
| Assunto: | Machine Learning Telecommunications Time Series Binary Classification Supervised Learning Feature-Based Analysis Imbalanced Data Decision Trees Ensemble Methods Random Forest AdaBoost XGBoo |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The telecommunication service providers seek an accurate insight into the devices that are connected within a home network, in order to provide a better in-home experience. In this way, the goal of the internship was to develop a machine learning model for fingerprinting of Amazon devices. This can be translated to a timeseries binary classification problem and assumes an exploration background of understanding the employment of bytes received by the router over time as an indicator of the internet usage to detect the Amazon devices. A feature-based analysis was conducted to make it possible to apply the most common and simple classifiers, which is relevant within a company context. The available data presented some challenges, namely a high imbalance and number of missing values. For this, it was studied several combinations of different techniques to increase the importance of the minority class and to impute the unknown values. In addition, multiple models were trained, whose results were evaluated and compared. The achieved performance of the best model was not considered satisfactory to correctly identify the Amazon devices, which lead to the conclusion that other approaches, algorithms and/or variable(s) need to be considered in a future iteration. The project contributed to a better understanding of the path to take on the identification of the devices and introduced new approaches and reasoning when dealing with similar data as the timeseries in analysis. |
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