Sökning: "Anomaly detection"
Visar resultat 21 - 25 av 337 uppsatser innehållade orden Anomaly detection.
21. Anomaly Detection for Network Traffic in a Resource Constrained Environment
Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknikSammanfattning : Networks connected to the internet are under a constant threat of attacks. To protect against such threats, new techniques utilising already connected hardware have in this thesis been proven to be a viable solution. LÄS MER
22. Football Trajectory Modeling Using Masked Autoencoders : Using Masked Autoencoder for Anomaly Detection and Correction for Football Trajectories
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Football trajectory modeling is a powerful tool for predicting and evaluating the movement of a football and its dynamics. Masked autoencoders are scalable self-supervised learners used for representation learning of partially observable data. LÄS MER
23. Cyber Threat Detection using Machine Learning on Graphs : Continuous-Time Temporal Graph Learning on Provenance Graphs
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Cyber attacks are ubiquitous and increasingly prevalent in industry, society, and governmental departments. They affect the economy, politics, and individuals. LÄS MER
24. Condition Monitoring Of Machine Components From Drive Data Using Semi-Supervised Anomaly Detection Methods
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : The mission of the machine manufacturer is to gain insights from machine data to increase their machines' efficiency and sustainability. Continuously monitoring the machine data with machine learning helps to detect emerging mechanical problems and prevents unexpected failures. LÄS MER
25. Unsupervised Anomaly Detection and Explainability for Ladok Logs
Master-uppsats, Umeå universitet/Institutionen för datavetenskapSammanfattning : Anomaly detection is the process of finding outliers in data. This report will explore the use of unsupervised machine learning for anomaly detection as well as the importance of explaining the decision making of the model. LÄS MER