Aleksander Vanberg Eriksen

Norwegian version of this page Faculty of Computer Science, Engineering and Economics

Norwegian version of this page Position

Doctoral Research Fellow

Contact

+4769608558

aleksander.v.eriksen@hiof.no

Study place

Halden

Office nr.

D1-021

About me

PhD Fellow from Fredrikstad with a background in Computer Science (BSc and MSc). My research focuses on energy-efficient scheduling for LoRa wireless networks. Previously, I’ve worked as an AI Engineer with Python and as a substitute teacher guiding Vg2 IT students through their projects.

When I’m not working, you’ll find me at the gym, out with friends, or at home with a movie or a game.

Github

Linkedln

PhD-programme

Digitalisation and Society

Academic interests

Machine Learning
IoT-systems
Cyber-Physical systems
Signal Processing and Communication across devices
Data processing
Integrating IT into healthcare

Background

BSc in design and development of IT-systems, specialication in Machine Learning (2022)
MSc in Applied Computer Science, specialication in Machine Learning and IoT systems (2024)

Projects

My master thesis was about creating a preprocessing framework for three IoT dataset containing network information regarding network attacks. The framework analyzed each dataset and created separate datasets for each one that was optimized towards applying machine learning models for binary and multi classification. The link to the Github page can be found here: Anomaly detection

Research groups

Cyber-physical systems

Publications

Scientific articles and book chapters

Pal, Kim Kristoffer; Eriksen, Aleksander Vanberg & Dinh, Thi Thuy Nga (2025). XGBoost Feature Selection for Multi-Class and Binary Classification on UNSW-NB15 Dataset. In IEEE (Eds.), 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331521165. doi: 10.1109/icce63647.2025.10930023. Full text in Research Archive Show summary
Network intrusion detection systems (NIDS) are a critical part of ensuring the integrity and reliability of a network, which is essential in secure communication. To achieve this, an efficient NIDS with as low complexity as possible is preferred. Therefore, this paper presents a feature selection (FS) method that utilizes extreme gradient boosting (XGBoost) to train binary and multi-class machine learning (ML) algorithms on the 19 most important features in the UNSW-NB15 dataset. The FS method is evaluated on seven ML algorithms namely Artificial Neural Network (ANN), Logistic Regression (LR), k-Nearest-Neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and XGBoost. Results from both the multi-class and binary classifications showed that RF and XGBoost had the highest accuracy and Fl-score at (77.14%, 78%), (87.445%, 87%), (78.156%, 79%) and (87.527%, 87%), respectively. Compared to existing works, most ML algorithms demonstrate better or similar performance on the UNSW-NB15 dataset. In addition, the proposed FS method significantly reduces the running time for XGBoost and RF by approximately 80% and 48%, respectively in both binary and multi-class classifications.

View all works in NVA

Published July 30, 2026 11:35 AM - Last modified Oct. 22, 2026 6:12 PM