document

Anomaly Detection in Sleep Staging in Critically Ill Children

Oerlemans, Carlijn (author)

Study objectives: Conventional sleep scoring is based on the scoring criteria of the American Association of Sleep Medicine (AASM) but may not be suited to describe sleep in critically ill children admitted to the Pediatric Intensive Care Unit (PICU). In this study, an anomaly detection model using Gaussian Models trained on sleep stages in data...

master thesis 2024

document

Logs to the Rescue: Creating meaningful representations from log files for Anomaly Detection

Timmerman, Gerben (author)

This thesis offers a comprehensive exploration of log-based anomaly detection within the domain of cybersecurity incident response. The research describes a different approach and explores relevant log features for language model training, experimentation with different language models and training methodologies, and the investigation of the...

master thesis 2023

document

Anomaly Detection in WAAM Deposition of Nickel Alloys: Single-Material and Cross-Material Analysis

Rajesh, Aditya (author)

The current research work investigates the possibility of using machine learning models to deduce the relationship between WAAM (wire arc additive manufacturing) sensor responses and defect presence in the printed part. The work specifically focuses on three materials from the nickel alloy family – Inconel 718, Invar 36 and Inconel 625, and uses...

master thesis 2023

document

VoBERT: Unstable Log Sequence Anomaly Detection: Introducing Vocabulary-Free BERT

Hofman, Daan (author)

With the ever-increasing digitalisation of society and the explosion of internet-enabled devices with the Internet of Things (IoT), keeping services and devices secure is becoming more important. Logs play a critical role in sustaining system reliability. Manual analysis of logs has become increasingly difficult, accelerating the development of...

master thesis 2023

document

AGONI: Adversarial Generation Of Network Intrusions

Thomas, Wessel (author)

Network Intrusion Detection Systems (NIDSs) defend our computer networks against malicious network attacks. Anomaly-based NIDSs use machine learning classifiers to categorise incoming traffic. Research has shown that classifiers are vulnerable to adversarial examples, perturbed inputs that lead the classifier into misclassifying the input....

master thesis 2023

document

A Digital Twin for Condition Monitoring of an Industrial Chain Belt Conveyor System

Verdoes, Max (author)

Chain belt conveyors have become a popular choice for transporting materials in diverse industries. The primary driving force behind these conveyors is the gearmotor, which is composed of an electric motor and a gear unit. The industrial electric motor market is estimated to be 21 billion USD and consumes up to 70\% of the electricity by the...

master thesis 2023

document

SAD: State machine-based Anomaly Detection in User Behavior

Nguyen, Charlie (author)

Over the past centuries, cybercrime has constantly grown. Among the most popular attacks against companies are phishing emails that especially gained popularity for threat actors to use as a tool during the COVID-19 pandemic. By changing the working environment, most communication channels between employees shifted from personal conversations...

master thesis 2022

document

Preserving Performance in Anomaly Detection Models for Real-Time Univariate Streams

Karpova, Natalia (author)

Anomaly detection has gathered plenty of attention in the previous years. However, there is little evidence of the fact that existing anomaly detection models could show similar performance on different streaming datasets. <br/>Within this study, we research the applicability of existing anomaly detectors to a wide range of univariate streams....

master thesis 2022

document

Robust Attack Graphs

Mouwen, Dennis (author)

Every day, Intrusion Detection Systems around the world generate huge amounts of data. This data can be used to learn attacker behaviour, such as Techniques, Tactics, and Procedures (TTPs). Attack Graphs (AGs) provide a visual way of describing these attack patterns. They can be generated without expert knowledge and vulnerability reports. The...

master thesis 2022

document

Locally Explainable Isolation Forest with Mixed-Attribute Data and Ternary Isolation Trees: Combatting Money Laundering with Anomaly Detection

Huistra, Mark (author)

In the fight against money laundering, demand for data-driven Anti-Money Laundering (AML) solutions is growing. Particularly anomaly detection algorithms have proven effective in the detection of suspicious customer behaviour, as well as observing patterns otherwise hidden in customer transaction data. In this thesis, the Isolation Forest...

master thesis 2021

document

Covert DNS Storage Channel Detection: Uncovering surreptitious data exchange using the phonebook of the internet

van Hal, Sven (author)

The cyber arms race has red and blue teams continuously at their toes to keep ahead. Increasingly capable cyber actors breach secure networks at a worrying scale. While network monitoring and analysis should identify blatant data exfiltration attempts, covert channels bypass these measures and facilitate surreptitious information extraction. The...

master thesis 2021

document

A personalized approach for communicating found anomalies in Netflow data to end-users

de Hoog, Dion (author)

In this research, we use different supervised and unsupervised machine learning techniques to detect anomalies in NetFlow data. We aim to create a system for home or small-business use where the user is in control. We use WEKA for the machine learning models and feature selection. The UGR’16 dataset is used to train and test the models. We...

master thesis 2021

document

Modeling and Detecting Anomalous Safety Events in Approach Flights Using ADS-B Data

Bonifazi, Alberto (author)

This thesis shows that it is possible to produce safety knowledge by mining Automatic Dependent Surveillance-Broadcast (ADS-B) data. The methodology combines exceedance detection and anomaly detection techniques to identify anomalous safety events in approach flights. One of these events is unstable approaches, which are identified with a rule...

master thesis 2020

document

Anomaly Detection in Intersection Control: Sliding Mode Observer Based Anomaly Detection in Virtual Platooning Enabled Intersection Control

Janse, Eline (author)

Road intersections have a large impact on road accidents and travel delay. Applying infrastructure, such as stop signs and traffic lights, are supposed to prevent collisions on the intersection. However, such methods contribute to the travel delay, while accidents still occur due to human errors. Considering the rise of automation within...

master thesis 2020

document

Probabilistic Quantification of Airspace Resilience

Janowski, Jakub (author)

The resilience of the Air Traffic Management (ATM) system to disturbances is required to maintain high operation performance. Before it can be improved, the resilience of the ATM system must be quantified. The measurement of resilience requires knowledge of a system reference state. This thesis proposes a novel methodology to detect disruptions...

master thesis 2020

document

Advanced Set Bounding Methods for Fault Detection

Ritsma, Folkert (author)

Performance of set based fault detection is highly dependent on the complexity of the set bounding methods used to bound the healthy residual set. Existing methods achieve robust performance with complex set bounding that narrowly define healthy system behavior, yet at the cost of higher computation times. In this thesis a major improvement is...

master thesis 2019

document

Anomaly detection and diagnosis in ASML event log using attentional LSTM network

SHI, XIAOTONG (author)

In the ASML test system, all activity events of the test are continuously recorded in event logs, and these logs are intended to help people diagnose the root cause of a failure. However, due to the large scale of the logs, manual inspection of these logs consumes lots of effort and time, and the lack of expert knowledge of engineers makes the...

master thesis 2019

document

Exploring normalizing flow for anomaly detection

Pathak, Chinmay (author)

Anomaly detection is a task of interest in many domains. Typical way of tackling this problem is using an unsupervised way. Recently, deep neural network based density estimators such as Normalizing flows have seen a huge interest. The ability of these models to do the exact latent-variable inference and exact log-likelihood calculation with...

master thesis 2019

document

From log files to train traffic reports : Using Natural Language Generation to explain anomalies from Train Control System log files

Urumovska, Bojana (author)

The Natural Language Generation field has advanced in generating human readable reports for domain experts in various fields. Nevertheless, Natural Language Generation and anomaly detection techniques have not been used in the rail domain yet. Currently, data analysis and incident reporting for log files from the train control system are...

master thesis 2019

document

Real-time anomaly detection in logs using rule mining and complex event processing at scale

Stavroulakis, Alexandros (author)

Log data, produced from every computer system and program, are widely used as source of valuable information to monitor and understand their behavior and their health. However, as large-scale systems generate a massive amount of log data every minute, it is impossible to detect the cause of system failure by examining manually this huge size of...

master thesis 2019