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Comparative aAnalysis of Unsupervised Anomaly Detection Methods for Credit Card Fraud Detection
Jůzová, Anna ; Červinka, Michal (advisor) ; Janásek, Lukáš (referee)
In recent years, the increasing rate of cashless payments and online pur- chases has led to a rise in credit card fraud. Detecting fraudulent transactions poses a signifcant challenge for fnancial institutions, however, machine learn- ing has emerged as a promising tool. This thesis focuses on machine learning models for anomaly detection that have not received sufcient attention in pre- vious research. Specifcally, the study examines Isolation Forest, Local Outlier Factors, and One-Class Support Vector Machine. These models identify fraud- ulent payments as transactions that do not ft the learned pattern from past transactions. To optimise performance, the data are normalised using diferent normalisation techniques. Among the tested models, the Local Outlier Factor model with data normalised using the min-max method seems to be the most efective. JEL Classifcation C49, G21, K42 Keywords credit card fraud, machine learning, anomaly de- tection, data normalisation Title Comparative Analysis of Unsupervised Anomaly Detection Methods for Credit Card Fraud De- tection Author's e-mail anna.juzova11@gmail.com Supervisor's e-mail michal.cervinka@fsv.cuni.cz
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Application Monitoring of IoT Devices
Krajč, Patrik ; Ryšavý, Ondřej (referee) ; Matoušek, Petr (advisor)
IoT devices use various standards at the level of the transmission medium and communication protocol. The aim of the work is to create a system, which we can unify a heterogeneous network of the Internet of Things for monitoring purposes. For data collection from the IoT network was used the Home Assistant platform which is uses SNMP agent we created. The monitoring system includes the Nagios core system, which is extended with machine learning-based anomaly detection.
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Data Mining Case Study in Python
Stoika, Anastasiia ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis focuses on basic concepts and techniques of the process known as knowledge discovery from data. The goal is to demonstrate available resources in Python, which enable to perform the steps of this process. The thesis addresses several methods and techniques focused on detection of unusual observations, based on clustering and classification. It discusses data mining task for data with the limited amount of inspection resources. This inspection activity should be used to detect unusual transactions of sales of some company that may indicate fraud attempts by some of its salespeople.
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Application Monitoring of IoT Devices
Krajč, Patrik ; Ryšavý, Ondřej (referee) ; Matoušek, Petr (advisor)
IoT devices use various standards at the level of the transmission medium and communication protocol. The aim of the work is to create a system, which we can unify a heterogeneous network of the Internet of Things for monitoring purposes. For data collection from the IoT network was used the Home Assistant platform which is uses SNMP agent we created. The monitoring system includes the Nagios core system, which is extended with machine learning-based anomaly detection.
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Data Mining Case Study in Python
Stoika, Anastasiia ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis focuses on basic concepts and techniques of the process known as knowledge discovery from data. The goal is to demonstrate available resources in Python, which enable to perform the steps of this process. The thesis addresses several methods and techniques focused on detection of unusual observations, based on clustering and classification. It discusses data mining task for data with the limited amount of inspection resources. This inspection activity should be used to detect unusual transactions of sales of some company that may indicate fraud attempts by some of its salespeople.
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