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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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Automatic Tire Inspection Using Surface Scans
Toth Vaňo, Pavol ; Materna, Zdeněk (referee) ; Španěl, Michal (advisor)
This thesis deals with automatic detection of defects on tire treads using their depth scans. The approach proposed in the thesis doesn’t require a faultless reference tire for the inspected tire. The first step is the detection of anomalies, which is done using a modification of the PatchCore method proposed in the thesis, taking advantage of the repetition of patterns on the tire tread. Subsequently, anomalies corresponding to special elements on the tire are detected using the deep neural networks Faster R-CNN and Deep Hough transform, and they are filtered out. Applying the proposed approach on the prepared dataset, the value 0.584 of Average Precision metric for detection was obtained. The biggest weakness of the proposed method is its limited ability to detect defects with a very small depth.
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Anomaly Detection in System Log Files Using Machine Learning
Moresová, Eva ; Burgetová, Ivana (referee) ; Matoušek, Petr (advisor)
Detekcia anomálií v logoch je dôležitý proces, ktorý pomáha detekovať poruchy systému, pokusy o prienik do systému a ďalšie škodlivé správanie, prípadne týmto udalostiam umožňu\-je predchádzať. Moderné systémy však produkujú logy v množstvách, ktoré nie je možné analyzovať ručne. Preto sa na tento účel používa množstvo automatizovaných metód, od techník založených na pravidlách, až po prístupy používajúce hlboké učenie. Cieľom tejto diplomovej práce je porovnať niekoľko metód detekcie anomálií v logoch a určiť, ktorá z nich je najviac vhodná pre použitie na veľkých log súboroch z praxe. Reprezentantom takýchto dát je zbierka logov z produkčného AAA servera, ktoré boli poskytnuté firmou AT&T. Okrem AT&T logov boli metódy aplikované a vyhodnotené na dvoch ďalších anotovaných datasetoch, z ktorých jeden bol obohatený o synteticky generované anomálie. Táto práca využíva tri metódy detekcie anomálií: lokálny odľahlý faktor, zhlukovací algoritmus DBSCAN a OPTICS framework. Prvé dve metódy skúmajú logy na úrovni jednotlivých záznamov, zatiaľ čo posledná analyzuje celé sekvencie logov. Všetky metódy dosiahli výsledky porovnateľné s prácami, ktoré realizujú podobné prístupy.
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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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