Anomaly, intrusion and fraud detection

Finding the rare event: network intrusion, fraudulent shops and trades, money laundering, and anomalies in industrial and maritime operational data. Mostly semi-supervised, because the interesting cases are the ones nobody labelled.

10 supervised theses. See all supervised theses for the full list, or open topics if you are looking for one to take on.

Level	Author	Title	Date	Industry	Recording
MSc	J. Haapanen	Anomaly Detection in Phase-Level Maritime Operational Data — A Comparative Study of Detection Methods	Aug. 2026	Napa Oy	video
MSc	A. Sarkima	Graph-based anomaly detection: Evaluating Logs2Graphs with non-benchmark logs	Mar. 2025
MSc	A. Bhat	Classifying Scam E-Commerce Shops with Supervised Learning	Oct. 2024	F-Secure
MSc	T. Brumani	Microservices-based autonomous anomaly detection for mobile network observability	Oct. 2023	Ericsson	video
MSc	Á. García Gutierrez	Anomaly detection on osmosis trades	Oct. 2023	Numia Data
MSc	T. Gyabaah	Artificial intelligence to support NFTs creation: Comparison of machine learning algorithms to detect fraud in artwork	Aug. 2022
MSc	T. Rahman	Intrusion detection system based on deep learning	Aug. 2022
MSc	M. Mishin	Anomaly detection algorithms and techniques for network intrusion detection systems	Aug. 2020	Ericsson	video
MSc	T. Hämmäinen	Clustering IoT devices for network intrusion detection systems	May 2020	Ericsson
MSc	M. Torres Porta	Anti-money laundering system based on customer behavior	Aug. 2019