Aleks Huč
T: +386 1 479 8243
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Office hours: Tuesday at 11am or by appointment in R3.50 (LeM)
Room: R3.50 - Laboratorij LEM

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.

Past projects

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,

2. Huč, A., & Trček, D. (2021). Anomaly detection in IoT networks: From architectures to machine learning transparency. IEEE Access, 9, 60607-60616,

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,


1. Huč, A. (2022). Detecting temporal and spatial anomalies in users’ activities for security provisioning in computer networks,

2. Huč, A. (2015). Napovedovanje mesta na RNA v interakciji s proteinom,

3. Huč, A. (2012). Izdelava in uporaba senčilnikov v sodobni računalniški grafiki : diplomsko delo. Univerza v Ljubljani, Fakulteta za računalništvo in informatiko,