Iglesias Vázquez, F., & Zseby, T. (2023). Temporal silhouette: validation of stream clustering robust to concept drift. Machine Learning. https://doi.org/10.1007/s10994-023-06462-2 ( reposiTUm)

Iglesias Vazquez, F., Hartl, A., Zseby, T., & Zimek, A. (2023). Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications, 233, Article 120994. https://doi.org/10.34726/4581 ( reposiTUm)

Iglesias, F., Meghdouri, F., Annessi, R., & Zseby, T. (2022). CCgen: Injecting Covert Channels into Network Traffic. Security and Communication Networks, 2022, 1–11. https://doi.org/10.1155/2022/2254959 ( reposiTUm)

Meghdouri, F., Iglesias Vazquez, F., & Zseby, T. (2022). Modeling Data with Observers. Intelligent Data Analysis, 26(3), 785–803. https://doi.org/10.3233/ida-215741 ( reposiTUm)

Iglesias, F., Zseby, T., & Zimek, A. (2021). Clustering Refinement. International Journal of Data Science and Analytics, 12(4), 333–353. https://doi.org/10.1007/s41060-021-00275-z ( reposiTUm)

Iglesias, F., Ferreira, D. C., Vormayr, G., Bachl, M., & Zseby, T. (2020). NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research. Applied Sciences, 10(12), 4307. https://doi.org/10.3390/app10124307 ( reposiTUm)

Iglesias, F., Zseby, T., & Zimek, A. (2019). Absolute Cluster Validity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(9), 2096–2112. https://doi.org/10.1109/tpami.2019.2912970 ( reposiTUm)

Iglesias, F., & Zseby, T. (2019). Pattern Discovery in Internet Background Radiation. IEEE Transactions on Big Data, 5(4), 467–480. https://doi.org/10.1109/tbdata.2017.2723893 ( reposiTUm)

Iglesias Vázquez, F., Zseby, T., Ferreira, D., & Zimek, A. (2019). MDCGen: Multidimensional Dataset Generator for Clustering. Journal of Classification. https://doi.org/10.1007/s00357-019-9312-3 ( reposiTUm)

Iglesias, F., Milosevic, J., & Zseby, T. (2018). Fuzzy classification boundaries against adversarial network attacks. Fuzzy Sets and Systems, 368, 20–35. https://doi.org/10.1016/j.fss.2018.11.004 ( reposiTUm)

Meghdouri, F., Zseby, T., & Iglesias Vazquez, F. (2018). Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic. Applied Sciences, 8(11), 1–16. https://doi.org/10.3390/app8112196 ( reposiTUm)

Martinasek, Z., Iglesias Vazquez, F., Malina, L., & Martinasek, J. (2017). Crucial pitfall of DPA Contest V4.2 Implementation. Security and Communication Networks, 9(18), 6094–6110. http://hdl.handle.net/20.500.12708/146246 ( reposiTUm)

Iglesias Vazquez, F., Annessi, R., & Zseby, T. (2017). Analytic Study of Features for the Detection of Covert Timing Channels in Network Traffic. Journal of Cyber Security and Mobility, 6(3), 245–270. https://doi.org/10.13052/2245-1439.632 ( reposiTUm)

Zseby, T., Iglesias Vazquez, F., Bernhardt, V., Frkat, D., & Annessi, R. (2016). A Network Steganography Lab on Detecting TCP/IP Covert Channels. IEEE Transactions on Education, 59(3), 224–232. https://doi.org/10.1109/te.2016.2520400 ( reposiTUm)

Iglesias, F., & Zseby, T. (2016). Time-activity footprints in IP traffic. Computer Networks, 107, 64–75. https://doi.org/10.1016/j.comnet.2016.03.012 ( reposiTUm)

Iglesias Vazquez, F., Annessi, R., & Zseby, T. (2016). DAT detectors: uncovering TCP/IP covert channels by descriptive analytics. Security and Communication Networks, 9(15), 3011–3029. http://hdl.handle.net/20.500.12708/148780 ( reposiTUm)

Iglesias, F., & Zseby, T. (2015). Analysis of network traffic features for anomaly detection. Machine Learning, 101(1–3), 59–84. https://doi.org/10.1007/s10994-014-5473-9 ( reposiTUm)

Zseby, T., Iglesias Vazquez, F., King, A., & Claffy, K. C. (2015). Teaching Network Security With IP Darkspace Data. IEEE Transactions on Education, 59(1), 1–7. https://doi.org/10.1109/te.2015.2417512 ( reposiTUm)

Iglesias Vazquez, F., & Zseby, T. (2015). Entropy-Based Characterization of Internet Background Radiation. Entropy. https://doi.org/10.3390/e17010074 ( reposiTUm)

Iglesias, F., & Kastner, W. (2013). Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies, 6(2), 579–597. https://doi.org/10.3390/en6020579 ( reposiTUm)

Iglesias Vazquez, F., Kastner, W., & Kofler, M. (2013). Holistic smart home models for air quality and thermal comfort management. Intelligent Decision Technologies, 7(1), 23–43. http://hdl.handle.net/20.500.12708/155306 ( reposiTUm)

Iglesias, F., & Palensky, P. (2013). Profile-based control for central domestic hot water distribution. IEEE Transactions on Industrial Informatics, 10(1), 697–705. https://doi.org/10.1109/tii.2013.2275032 ( reposiTUm)

Iglesias, F., Palensky, P., Cantos, S., & Kupzog, F. (2012). Demand side management for stand-alone hybrid power systems based on load identification. Energies, 5(11), 4517–4532. https://doi.org/10.3390/en5114517 ( reposiTUm)

Reinisch, C., Kofler, M., Iglesias, F., & Kastner, W. (2011). ThinkHome Energy Efficiency in Future Smart Homes. EURASIP Journal on Embedded Systems, 2011(1), 104617. https://doi.org/10.1155/2011/104617 ( reposiTUm)

Hartl, A., Iglesias Vazquez, F., & Zseby, T. (2024). dSalmon: High-Speed Anomaly Detection for Evolving Multivariate Data Streams. In E. Kalyvianaki & M. Paolieri (Eds.), Performance Evaluation Methodologies and Tools: 16th EAI International Conference, VALUETOOLS 2023, Crete, Greece, September 6–7, 2023, Proceedings (pp. 153–169). Springer Cham. https://doi.org/10.1007/978-3-031-48885-6_10 ( reposiTUm)

Iglesias Vazquez, F., Zseby, T., Hartl, A., & Zimek, A. (2023). SDOclust: Clustering with Sparse Data Observers. In O. Pedreira & V. Estivill-Castro (Eds.), Similarity Search and Applications : 16th International Conference, SISAP 2023, A Coruña, Spain, October 9–11, 2023, Proceedings (pp. 185–199). Springer. https://doi.org/10.1007/978-3-031-46994-7_16 ( reposiTUm)

Meghdouri, F., Vazquez, F. I., & Zseby, T. (2021). Shedding Light in the Tunnel: Counting Flows in Encrypted Network Traffic. In 2021 International Conference on Data Mining Workshops (ICDMW) (pp. 798–804). IEEE. https://doi.org/10.1109/icdmw53433.2021.00103 ( reposiTUm)

Iglesias, F., Hartl, A., Zseby, T., & Zimek, A. (2020). Are Network Attacks Outliers? A Study of Space Representations and Unsupervised Algorithms. In Machine Learning and Knowledge Discovery in Databases (pp. 159–175). Communications in Computer and Information Science. https://doi.org/10.1007/978-3-030-43887-6_13 ( reposiTUm)

Meghdouri, F., Iglesias Vazquez, F., & Zseby, T. (2020). Anomaly Detection for Mixed Packet Sequences. In Proceedings of the 45th LCN Symposium 2020 (pp. 120–130). http://hdl.handle.net/20.500.12708/77208 ( reposiTUm)

Hartl, A., Iglesias Vazquez, F., & Zseby, T. (2020). SDOstream: Low-Density Models for Streaming Outlier Detection. In ESANN 2020 - Proceedings (pp. 661–666). i6doc.com. http://hdl.handle.net/20.500.12708/77182 ( reposiTUm)

Iglesias Vazquez, F., Zseby, T., & Zimek, A. (2020). Interpretability and Refinement of Clustering. In Proceedings of the 7th DSAA 2020 (pp. 21–29). http://hdl.handle.net/20.500.12708/77183 ( reposiTUm)

Iglesias, F., Ojdanic, D., Hartl, A., & Zseby, T. (2020). MDCStream. In Proceedings of the 13th EAI International Conference on Performance Evaluation Methodologies and Tools. EAI Valuetools 2020, Tsukuba, Japan, Non-EU. Association for Computing Machinery. https://doi.org/10.1145/3388831.3388832 ( reposiTUm)

Meghdouri, F., Iglesias Vazquez, F., & Zseby, T. (2020). Cross-Layer Profiling of Encrypted Network Data for Anomaly Detection. In Proceedings of the 7th DSAA 2020 (pp. 469–478). http://hdl.handle.net/20.500.12708/77207 ( reposiTUm)

Ferreira, D. C., Vazquez, F. I., & Zseby, T. (2019). Extreme Dimensionality Reduction for Network Attack Visualization with Autoencoders. In 2019 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks IJCNN, Rio de Janeiro, Brazil, Non-EU. https://doi.org/10.1109/ijcnn.2019.8852056 ( reposiTUm)

Iglesias Vazquez, F., Zseby, T., & Zimek, A. (2018). Outlier Detection Based on Low Density Models. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). ICDM Workshop on Data Science and Big Data Analytics (DSBDA-2018), IEEE Internation Conference on Data Mining (ICDM-2018), Singapore, Non-EU. IEEE Computer Society Press. https://doi.org/10.1109/icdmw.2018.00140 ( reposiTUm)

Iglesias Vazquez, F., Bernhardt, V., Annessi, R., & Zseby, T. (2017). Decision Tree Rule Induction for Detecting Covert Timing Channels in TCP/IP Traffic. In Proceedings of the Machine Learning and Knowledge Extraction: First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference (pp. 1–19). http://hdl.handle.net/20.500.12708/75688 ( reposiTUm)

Cavaco Ferreira, D. L., Iglesias Vazquez, F., Vormayr, G., Bachl, M., & Zseby, T. (2017). A Meta-Analysis Approach for Feature Selection in Network Traffic Research. In Proceedings of the Reproducibility Workshop (Reproducibility´17, ACM SIGCOMM) (pp. 1–4). http://hdl.handle.net/20.500.12708/75686 ( reposiTUm)

Iglesias Vazquez, F., & Zseby, T. (2017). Are Network Covert Timing Channels Statistical Anomalies? In Proceedings of the 12th International Conference on Availability, Reliability and Security (ARES’17), Workshop on Criminal Use of Information Hiding (CUIng) (pp. 1–9). http://hdl.handle.net/20.500.12708/75687 ( reposiTUm)

Iglesias, F., & Zseby, T. (2014). Modelling IP darkspace traffic by means of clustering techniques. In 2014 IEEE Conference on Communications and Network Security. Communications and Network Security (CNS), 2014 IEEE Conference on, San Francisco, USA, Non-EU. https://doi.org/10.1109/cns.2014.6997483 ( reposiTUm)

Kofler, M., Iglesias Vazquez, F., & Kastner, W. (2013). An ontology for representation of user habits and building context in future smart homes. In Proceedings EG-ICE 2013 (pp. 1–10). http://hdl.handle.net/20.500.12708/73615 ( reposiTUm)

Iglesias Vazquez, F., & Kastner, W. (2013). A Global Approach of Habit Profiles for Smart Home Control. In Proceedings BS2013 (p. 8). http://hdl.handle.net/20.500.12708/73614 ( reposiTUm)

Iglesias Vazquez, F., & Kastner, W. (2012). Detecting user dissatisfaction in ambient intelligence environments. In Proceedings IEEE 17th Conference on Emerging Technologies Factory Automation (ETFA) (p. 4). http://hdl.handle.net/20.500.12708/54466 ( reposiTUm)

Iglesias Vazquez, F., & Kastner, W. (2012). Thermal comfort support application for smart home control. In Proceeedings 3rd International Symposium on Ambient Intelligence (ISAmI 2012) (pp. 109–118). Springer Berlin / Heidelberg. http://hdl.handle.net/20.500.12708/54476 ( reposiTUm)

Iglesias Vazquez, F., Kastner, W., & Reinisch, C. (2011). Impact of user habits in smart home control. In 16th IEEE International Conference on Emerging Technologies and Factory Automation (p. 8). http://hdl.handle.net/20.500.12708/53985 ( reposiTUm)

Iglesias Vazquez, F., & Kastner, W. (2011). Clustering methods for occupancy prediction in smart home control. In Proceedings ISIE 2011 (pp. 1321–1328). IEEE. http://hdl.handle.net/20.500.12708/53973 ( reposiTUm)

Iglesias Vazquez, F., Gaceo, S. C., Kastner, W., & Morales, J. A. M. (2011). Behavioral Profiles for Building Energy Performance Using eXclusive SOM. In IFIP Advances in Information and Communication Technology (pp. 31–40). http://hdl.handle.net/20.500.12708/53984 ( reposiTUm)

Iglesias Vazquez, F., Kastner, W., Gaceo, S. C., & Reinisch, C. (2011). Electricity Load Management in Smart Home Control. In 12th Conference of 12th Conference of International Building Performance Simulation Association (pp. 957–964). http://hdl.handle.net/20.500.12708/54042 ( reposiTUm)

Iglesias Vazquez, F., & Kastner, W. (2010). Usage Profiles for Sustainable Buildings. In 15th IEEE Conference on Emerging Techonologies and Factory Automation (pp. 1–8). http://hdl.handle.net/20.500.12708/53374 ( reposiTUm)

Iglesias Vazquez, F. (2024, March 13). Clustering and Anomaly Detection: Deep or Shallow Learning? [Presentation]. Universitat Politecnica de Catalunya UPC Conference/Seminar, Barcelona, Spain. ( reposiTUm)

Iglesias Vazquez, F. (2023, June 15). Unsupervised Learning in Streaming Data Analysis: Insights and Challenges [Presentation]. Expert Days Seminar, Orleans, France. ( reposiTUm)

Iglesias Vazquez, F. (2023, October 5). Data Integration Engineering for Boosting Digital Twins AI [Presentation]. Le Studium THURSDAY, Orleans, France. ( reposiTUm)

Iglesias Vazquez, F. (2023, November 28). Deep and Shallow Unsupervised Learning: Competing or Complementary? [Presentation]. Axis 4 Meetings - Machine Learning for Image Apllications, Orléans, France. ( reposiTUm)

Iglesias Vazquez, F. (2016). Methodology for Data Analysis and Machine Learning Application. Network Traffic Analysis and Anomaly Detection, Telecommunications Graduate Initiative (TGI) Course, Waterford, Ireland, EU. http://hdl.handle.net/20.500.12708/90694 ( reposiTUm)

Iglesias Vazquez, F. (2015). Knowledge Discovery by Cluster Analysis. The Department of Computer Architecture (DAC), The Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain, EU. http://hdl.handle.net/20.500.12708/90526 ( reposiTUm)

Zseby, T., & Iglesias Vazquez, F. (2012). Teaching Network Security with IP Darkspace Data. 2nd International Workshop on Darkspace and UnSolicited Traffic Analysis (DUST 2019), San Diego, CA, USA, Non-EU. http://hdl.handle.net/20.500.12708/91254 ( reposiTUm)

Iglesias Vázquez, F. (2012). Smart home control based on behavioural profiles [Dissertation, Technische Universität Wien]. reposiTUm. https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-51273 ( reposiTUm)