My main research area is incremental unsupervised methods for learning from data streams. Specifically, I work with the unsupervised detection of security anomalies in network data streams using incremental clustering. I also do research in the areas of machine learning, the Internet of Things (IoT), computer security, and data modeling.
Scientific papers:
1. Huč, A., Šalej, J., & Trebar, M. (2021). Analysis of machine learning algorithms for anomaly detection on edge devices. Sensors, 21(14), 4946, https://www.mdpi.com/1424-8220/21/14/4946.
2. Huč, A., & Trček, D. (2021). Anomaly detection in IoT networks: From architectures to machine learning transparency. IEEE Access, 9, 60607-60616, https://ieeexplore.ieee.org/document/9406023.
3. Huč, A., Vidrih, R., & Trebar, M. (2020). Determination of pears ripening stages based on electrochemical ethylene sensor. IEEE Sensors Journal, 20(23), 13976-13983, https://ieeexplore.ieee.org/document/9011581.
Theses:
1. Huč, A. (2022). Detecting temporal and spatial anomalies in users’ activities for security provisioning in computer networks, https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=137562.
2. Huč, A. (2015). Napovedovanje mesta na RNA v interakciji s proteinom, https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=72036.
3. Huč, A. (2012). Izdelava in uporaba senčilnikov v sodobni računalniški grafiki : diplomsko delo. Univerza v Ljubljani, Fakulteta za računalništvo in informatiko, https://repozitorij.uni-lj.si/IzpisGradiva.php?id=25673.