@unpublished{upm68231,
            year = {2021},
          author = {Luis de Marcos Mazar{\'i}o},
           title = {Detecci{\'o}n de tr{\'a}fico de criptominado con Machine Learning},
           month = {July},
         address = {Madrid},
        keywords = {Criptograf{\'i}a; Criptominado; Ciberataque; Cryptojacking},
             url = {https://oa.upm.es/68231/},
        abstract = {La creciente popularidad de las criptomonedas ha promovido el aumento de actividades de criptominado entre los usuarios. Debido al gran coste computacional que se requiere, han surgido los ataques de criptominado que buscan hacer uso de dispositivos ajenos con el fin de acelerar este proceso. El objetivo del trabajo presente es desarrollar un sistema de detecci{\'o}n de tr{\'a}fico de criptominado en tiempo real. Para ello, se han realizado experimentos de generaci{\'o}n de tr{\'a}fico en un entorno controlado de los que se han extra{\'i}do un conjunto de datos con informaci{\'o}n de red. Los datos obtenidos se han usado para el entrenamiento de algoritmos de predicci{\'o}n basados en Machine Learning. Los resultados del trabajo demuestran la capacidad de detectar el tr{\'a}fico de criptominado en tiempo real con gran precisi{\'o}n.
Abstract:
The growing popularity of cryptocurrencies has promoted the increase of cryptocurrency activities among users. Due to the large computational cost required, cryptomining attacks have emerged that seek to make use of foreign devices in order to accelerate this process. The objective of the present work is to develop a real-time cryptomining traffic detection system. For this purpose, traffic generation experiments have been carried out in a controlled environment from which a data set with network information has been extracted. The data obtained have been used for the training of prediction algorithms based on Machine Learning. The results of the work demonstrate the ability to detect cryptomining traffic in real time with high accuracy.}
}