Novel applications of Machine Learning to Network Traffic Analysis

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Novel applications of Machine Learning to Network Traffic Analysis ( novel-applications-machine-learning-network-traffic-analysis )

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other cases, or for some functions they require an important manual/supervision labour. This opens the opportunity for the application of ML algorithms for statistical analysis and logs data mining [58]. • Explore the necessary measures that must be implemented to ensure that an intrusion detection algorithm is not attacked by people/groups who wish to change its intended behaviour. Many machine learning algorithms (e.g. deep learning) are “black boxes”, whose decisions are difficult or impossible to interpret. This opens the door to attacks that are not based on modifying an algorithm that already works, but on changing very slightly the features used as predictors to produce a huge impact on the decision made by the algorithm. These attacks may go unnoticed by the user. This is an important area of research in other fields, as image processing [149] and also attracts interest in the area of safety-critical environments [150]. An area of related interest are the measures necessary to implement fair systems in terms of racial discrimination or other possible areas of discrimination [151]. In this case the emphasis is placed on the selection of the dataset used for training and/or on constraints in the optimization of the algorithm. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 84

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