Iglesias Vazquez, F., Marques, H. O., Zimek, A., & Zseby, T. (2025). What do anomaly scores actually mean? Dynamic characteristics beyond accuracy. Data Mining and Knowledge Discovery, 39(1), 1–59. https://doi.org/10.1007/s10618-024-01077-0 ( reposiTUm)

Brenner, B., Fabini, J., Offermanns, M., Semper, S., & Zseby, T. (2024). Malware communication in smart factories: A network traffic data set. Computer Networks, 255, Article 110804. https://doi.org/10.1016/j.comnet.2024.110804 ( reposiTUm)

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)

Brenner, B., Hollerer, S., Bhosale, P., Sauter, T., Kastner, W., Fabini, J., & Zseby, T. (2023). Better Safe Than Sorry: Risk Management based on a Safety-augmented Network Intrusion Detection System. IEEE Open Journal of the Industrial Electronics Society. https://doi.org/10.1109/OJIES.2023.3297057 ( reposiTUm)

Iglesias Vázquez, 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)

Vormayr, G., Fabini, J., & Zseby, T. (2020). Why are My Flows Different? A Tutorial on Flow Exporters. IEEE Communications Surveys and Tutorials, 22(3), 2064–2103. https://doi.org/10.1109/comst.2020.2989695 ( 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)

Eder-Neuhauser, P., Zseby, T., & Fabini, J. (2018). Malware propagation in smart grid networks: metrics, simulation and comparison of three malware types. Journal of Computer Virology and Hacking Techniques, 15(2), 109–125. https://doi.org/10.1007/s11416-018-0325-y ( 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)

Xypolytou, E., Gawlik, W., Zseby, T., & Fabini, J. (2018). Impact of Asynchronous Renewable Generation Infeed on Grid Frequency: Analysis Based on Synchrophasor Measurements. Sustainability, 10(5), 1–10. https://doi.org/10.3390/su10051605 ( reposiTUm)

Meghdouri, F., Zseby, T., & Iglesias Vázquez, 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)

Hartl, A., Annessi, R., & Zseby, T. (2018). Subliminal Channels in High-Speed Signatures. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 9(1), 30–53. https://doi.org/10.22667/JOWUA.2018.03.31.030 ( reposiTUm)

Eder-Neuhauser, P., Zseby, T., & Fabini, J. (2018). Malware propagation in smart grid monocultures. Elektrotechnik Und Informationstechnik : E & i, 135(3), 264–269. https://doi.org/10.1007/s00502-018-0616-5 ( reposiTUm)

Ullrich, J., Zseby, T., Fabini, J., & Weippl, E. (2017). Network-Based Secret Communication in Clouds: A Survey. IEEE Communications Surveys and Tutorials, 19(2), 1112–1144. https://doi.org/10.1109/comst.2017.2659646 ( reposiTUm)

Vormayr, G., Zseby, T., & Fabini, J. (2017). Botnet Communication Patterns. IEEE Communications Surveys and Tutorials, 19(4), 2768–2796. https://doi.org/10.1109/comst.2017.2749442 ( reposiTUm)

Eder-Neuhauser, P., Zseby, T., Fabini, J., & Vormayr, G. (2017). Cyber attack models for smart grid environments. Sustainable Energy, Grids and Networks, 12, 10–29. https://doi.org/10.1016/j.segan.2017.08.002 ( 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)

Xypolytou, E., Fabini, J., Gawlik, W., & Zseby, T. (2017). The FUSE testbed: establishing a microgrid for smart grid security experiments. Elektrotechnik Und Informationstechnik : E & i, 134(1), 30–35. https://doi.org/10.1007/s00502-017-0483-5 ( 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)

Eder-Neuhauser, P., Zseby, T., & Fabini, J. (2016). Resilience and security: a qualitative survey of urban smart grid architectures. IEEE Access. https://doi.org/10.1109/ACCESS.2016.2531279 ( 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)

Fabini, J., & Zseby, T. (2016). The Right Time: Reducing Effective End-to-End Delay in Time-Slotted Packet-Switched Networks. IEEE/ACM Transactions on Networking, 24(4), 2251–2263. https://doi.org/10.1109/tnet.2015.2451708 ( 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)

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

Casas, P., D´Alconzo, A., Fiadino, P., Bär, A., Finamore, A., & Zseby, T. (2014). When YouTube doesn’t Work - Analysis of QoE-relevant Degradation in Google CDN Traffic. IEEE Transactions on Network and Service Management, 11(4), 441–457. https://doi.org/10.1109/tnsm.2014.2377691 ( reposiTUm)

Zseby, T., & Fabini, J. (2014). Security Challenges for Wide Area Monitoring in Smart Grids. Elektrotechnik Und Informationstechnik : E & i, 131(3), 105–111. https://doi.org/10.1007/s00502-014-0203-3 ( reposiTUm)

Zseby, T., Fabini, J., & Rani, D. (2014). Synchrophasor Communication. Elektrotechnik Und Informationstechnik : E & i, 131(1), 8–13. https://doi.org/10.1007/s00502-013-0193-6 ( reposiTUm)

Zseby, T., & claffy, kc. (2012). Workshop Report: Darkspace and Unsolicited Traffic Analysis (DUST 2012). ACM SIGCOMM Computer Communication Review, 42(5), 49–53. https://doi.org/10.1145/2378956.2378965 ( reposiTUm)

Hartl, A., Iglesias Vazquez, F., & Zseby, T. (2025). Detection of Periodical Patterns and Contextual Anomalies in Data Streams. In F. Naretto & R. Pellungrini (Eds.), DS-LB 2024 : DS Late Breaking Contributions 2024. CEUR-WS. https://doi.org/10.34726/8960 ( reposiTUm)

Geiginger, L.-M., & Zseby, T. (2024). Evading Botnet Detection. In SAC ’24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (pp. 1331–1340). https://doi.org/10.1145/3605098.3635921 ( 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, F., Martínez, C., & Zseby, T. (2024). Impact of the Neighborhood Parameter on Outlier Detection Algorithms. In E. Chavez, B. Kimia, J. Lokoc, M. Patella, & J. Sedmidubsky (Eds.), Similarity Search and Applications : 17th International Conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, Proceedings (pp. 88–96). Springer. https://doi.org/10.1007/978-3-031-75823-2_8 ( 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)

Hartl, A., Fabini, J., & Zseby, T. (2022). Separating Flows in Encrypted Tunnel Traffic. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 609–616). IEEE. https://doi.org/10.1109/ICMLA55696.2022.00094 ( reposiTUm)

Fabini, J., Hartl, A., Meghdouri, F., Breitenfellner, C., & Zseby, T. (2021). SecTULab: A Moodle-Integrated Secure Remote Access Architecture for Cyber Security Laboratories. In The 16th International Conference on Availability, Reliability and Security. The 16th International Conference on Availability, Reliability and Security (ARES 2021), Vienna, Austria, Austria. Association for Computing Machinery. https://doi.org/10.1145/3465481.3470034 ( reposiTUm)

Hartl, A., Fabini, J., Roschger, C., Eder-Neuhauser, P., Petrovic, M., Tobler, R., & Zseby, T. (2021). Subverting Counter Mode Encryption for Hidden Communication in High-Security Infrastructures. In The 16th International Conference on Availability, Reliability and Security. The 16th International Conference on Availability, Reliability and Security (ARES 2021), Vienna, Austria, Austria. Association for Computing Machinery. https://doi.org/10.1145/3465481.3470082 ( 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)

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)

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)

Meghdouri, F., Bachl, M., & Zseby, T. (2020). EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection. In 2020 4th Cyber Security in Networking Conference (CSNet). 2020 4th Cyber Security in Networking Conference (CSNet), Lausanne, Switzerland. IEEE. https://doi.org/10.1109/csnet50428.2020.9265467 ( reposiTUm)

Bachl, M., Fabini, J., & Zseby, T. (2020). LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning. In 2020 IEEE 45th Conference on Local Computer Networks (LCN). 45th IEEE Conference on Local Computer Networks (LCN), Sydney, Australia. IEEE. https://doi.org/10.1109/lcn48667.2020.9314771 ( 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)

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)

Bachl, M., Meghdouri, F., Fabini, J., & Zseby, T. (2020). SparseIDS: Learning Packet Sampling with Reinforcement Learning. In 2020 IEEE Conference on Communications and Network Security (CNS). IEEE SPC 2020 - Sixth Workshop on Security and Privacy in the Cloud (SPC), Avignon, France. IEEE. https://doi.org/10.1109/cns48642.2020.9162253 ( reposiTUm)

Hartl, A., Bachl, M., Fabini, J., & Zseby, T. (2020). Explainability and Adversarial Robustness for RNNs. In 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService). The Sixth IEEE International Conference on Big Data Computing Service and Machine Learning Applications, Oxford, United Kingdom of Great Britain and Northern Ireland (the). IEEE. https://doi.org/10.1109/bigdataservice49289.2020.00030 ( 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. Association for Computing Machinery. https://doi.org/10.1145/3388831.3388832 ( reposiTUm)

Hartl, A., Zseby, T., & Fabini, J. (2019). BeaconBlocks: Augmenting Proof-of-Stake with On-Chain Time Synchronization. In 2019 IEEE International Conference on Blockchain (Blockchain). The 2nd IEEE International Conference on Blockchain (Blockchain-2019), Atlanta, United States of America (the). IEEE. https://doi.org/10.1109/blockchain.2019.00055 ( reposiTUm)

Bachl, M., Fabini, J., & Zseby, T. (2019). Cocoa. In Proceedings of the 2019 Workshop on Buffer Sizing. Workshop on Buffer Sizing, Palo Alto, CA, United States of America (the). ACM. https://doi.org/10.1145/3375235.3375236 ( reposiTUm)

Bachl, M., Hartl, A., Fabini, J., & Zseby, T. (2019). Walling up Backdoors in Intrusion Detection Systems. In Proceedings of the 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks. Big-DAMA ’19, Orlando, FL, United States of America (the). ACM. https://doi.org/10.1145/3359992.3366638 ( reposiTUm)

Bachl, M., Zseby, T., & Fabini, J. (2019). Rax: Deep Reinforcement Learning for Congestion Control. In 2019 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE. http://hdl.handle.net/20.500.12708/76606 ( 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. https://doi.org/10.1109/ijcnn.2019.8852056 ( reposiTUm)

Annessi, R., Fabini, J., & Zseby, T. (2018). To Trust or Not to Trust. In Proceedings of the 13th International Conference on Availability, Reliability and Security. 13th International Conference on Availability, Reliability and Security, Hamburg, Germany. ACM. https://doi.org/10.1145/3230833.3233252 ( reposiTUm)

Paudel, S., Smith, P., & Zseby, T. (2018). Stealthy Attacks on Smart Grid PMU State Estimation. In Proceedings of the 13th International Conference on Availability, Reliability and Security. 13th International Conference on Availability, Reliability and Security (ARES 2018), Hamburg, Germany. ACM. https://doi.org/10.1145/3230833.3230868 ( 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. IEEE Computer Society Press. https://doi.org/10.1109/icdmw.2018.00140 ( reposiTUm)

Annessi, R., Zseby, T., & Fabini, J. (2018). A new Direction for Research on Data Origin Authentication in Group Communication. In Cryptology and Network Security (pp. 515–525). Springer. https://doi.org/10.1007/978-3-030-02641-7_26 ( reposiTUm)

Frkat, D., Annessi, R., & Zseby, T. (2018). ChainChannels: Private Botnet Communication Over Public Blockchains. In The 2018 IEEE International Conference on Blockchain (Blockchain-2018) (pp. 1244–1252). http://hdl.handle.net/20.500.12708/76482 ( reposiTUm)

Zseby, T. (2017). Smarter than the Grid: Malware Communication Trends. In ComForEn 2017 - 8. Fachtagung - Communications for Energy Systems (p. 71). OVE, Austrian Electrotechnical Association. http://hdl.handle.net/20.500.12708/75882 ( reposiTUm)

Annessi, R., Fabini, J., & Zseby, T. (2017). It’s about Time: Securing Broadcast Time Synchronization with Data Origin Authentication. In 2017 26th International Conference on Computer Communication and Networks (ICCCN). International Conference on Computer Communication and Networks (ICCCN), Vancouver, Canada. IEEE. https://doi.org/10.1109/icccn.2017.8038418 ( reposiTUm)

Hartl, A., Annessi, R., & Zseby, T. (2017). A Subliminal Channel in EdDSA. In Proceedings of the 2017 International Workshop on Managing Insider Security Threats. ACM CCS International Workshop on Managing Insider Security Threats, Dallas, Texas, USA, Non-EU. ACM. https://doi.org/10.1145/3139923.3139925 ( reposiTUm)

Xypolytou, E., Zseby, T., Fabini, J., & Gawlik, W. (2017). Detection and Mitigation of Cascading Failures in Interconnected Power Systems. In ISGT Europe 2017 (pp. 1–6). IEEE. http://hdl.handle.net/20.500.12708/75621 ( 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)

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)

Eder-Neuhauser, P., & Zseby, T. (2017). The Art of Defending Critical Infrastructures. In ISGT-Europe, IEEE Conference (pp. 1–6). http://hdl.handle.net/20.500.12708/75771 ( reposiTUm)

Paudel, S., Smith, P., & Zseby, T. (2017). Attack Models for Advanced Persistent Threats in Smart Grid Wide Area Monitoring. In 2nd Workshop on Cyber-Physical Security and Resilience in Smart Grids (pp. 1–6). http://hdl.handle.net/20.500.12708/75615 ( 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)

Krieg, C., Wolf, C., Jantsch, A., & Zseby, T. (2017). Toggle MUX. In Proceedings of the 54th Annual Design Automation Conference 2017. Design Automation Conference (DAC), Austin, United States of America (the). ACM. https://doi.org/10.1145/3061639.3062328 ( reposiTUm)

Eder-Neuhauser, P., Zseby, T., & Fabini, J. (2016). Simulations On Resilience And Malware Containment In Smart Grid Communication Architectures. In VSS - VIENNA young SCIENTISTS SYMPOSIUM, June 9-10 2016 (pp. 88–89). Book-of-Abstracts.com, Heinz A. Krebs. http://hdl.handle.net/20.500.12708/75145 ( reposiTUm)

Casas, P., D’Alconzo, A., Zseby, T., & Mellia, M. (2016). Big-DAMA. In Proceedings of the 2016 workshop on Fostering Latin-American Research in Data Communication Networks. LANCOMM ’16, Florianópolis, Brazil, Non-EU. https://doi.org/10.1145/2940116.2940117 ( reposiTUm)

Paudel, S., Smith, P. N., & Zseby, T. (2016). Data Integrity Attacks in Smart Grid Wide Area Monitoring. In T. Brandstetter, H. Janicke, & K. Jones (Eds.), Electronic Workshops in Computing. British Computer Society. https://doi.org/10.14236/ewic/ics2016.9 ( reposiTUm)

Meisel, M., Wilker, S., Fabini, J., Annessi, R., Zseby, T., Müllner, M., Kastner, W., Litzlbauer, M., Gawlik, W., & Neureiter, C. (2016). Methodical Reference Architecture Development Progress. In F. Kupzog (Ed.), energieinformatik 2016 (pp. 40–43). Österreichischer Verband für Elektrotechnik. http://hdl.handle.net/20.500.12708/75506 ( reposiTUm)

Fabini, J., & Zseby, T. (2015). M2M Communication Delay Challenges: Application and Measurement Perspectives. In Proceedings of the 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1859–1864). http://hdl.handle.net/20.500.12708/74551 ( reposiTUm)

Zseby, T., Brownlee, N., King, A., & claffy, kc. (2014). Nightlights: Entropy-Based Metrics for Classifying Darkspace Traffic Patterns. In Passive and Active Measurement (pp. 275–277). Lecture Notes in Computer Science Volume 8362. https://doi.org/10.1007/978-3-319-04918-2_30 ( reposiTUm)

Fabini, J., Zseby, T., & Hirschbichler, M. (2014). Representative Delay Measurements (RDM): Facing the Challenge of Modern Networks. In Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools. ValueTools 2014, Bratislava, Slovakia, EU. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). https://doi.org/10.4108/icst.valuetools.2014.258181 ( 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)

Zseby, T. (2013). Is IPv6 Ready for the Smart Grid? In 2012 International Conference on Cyber Security. ASE International Conference on Cyber Security 2012, Washington, DC, Non-EU. IEEE Computer Society Conference Publishing Services (CPS). https://doi.org/10.1109/cybersecurity.2012.27 ( reposiTUm)

Zseby, T. (2013). Secure Communication in Smart Grids. In F. Kupzog (Ed.), Tagungsband ComForEn 2013 Vierte Fachkonferenz Kommunikation für Energiesysteme (p. 6). OVE-Schriftenreihe. http://hdl.handle.net/20.500.12708/73767 ( reposiTUm)

Zseby, T., King, A., Brownlee, N., & Claffy, K. C. (2013). The Day after Patch Tuesday: Effects Observable in IP Darkspace Traffic. In Passive and Active Measurement (pp. 273–275). Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-36516-4_32 ( reposiTUm)

Henke, C., Wuttke, R., Zseby, T., & Campowsky, K. (2012). On Creating Overlay Routing Topologies between Heterogeneous Experimental Facilities. In T. Korakis, H. Li, P. Tran-Gia, & H.-S. Park (Eds.), Testbeds and Research Infrastructure: Development of Networks and Communities 7th International ICST Conference, TridentCom 2011 (pp. 162–171). Springer. https://doi.org/10.1007/978-3-642-29273-6_13 ( reposiTUm)

Zinner, T., Klein, D., Tutschku, K., Zseby, T., Tran-Gia, P., & Shavitt, Y. (2011). Performance of concurrent multipath transmissions — Measurements and model validation. In 2011 7th EURO-NGI Conference on Next Generation Internet Networks. 7th EURO-NF Conference on Next Generation Internet (NGI 2011), Kaiserslautern, Germany, EU. https://doi.org/10.1109/ngi.2011.5985862 ( reposiTUm)

Zinner, T., Tutschku, K., & Zseby, T. (2011). MultiNext — Measuring concurrent multipath transmissions in an experimental facility. In 2011 7th EURO-NGI Conference on Next Generation Internet Networks. 7th EURO-NF Conference on Next Generation Internet (NGI 2011), Kaiserslautern, Germany, EU. https://doi.org/10.1109/ngi.2011.5985870 ( reposiTUm)

Kolisnyk, M., Jantsch, A., Zseby, T., & Kharchenko, V. (2023). Markov Model of PLC Availability Considering Cyber-Attacks in Industrial IoT. In C. van Gulijk, E. Zaitseva, & M. Kvassay (Eds.), Reliability Engineering and Computational Intelligence for Complex Systems : Design, Analysis and Evaluation (Vol. 496, pp. 61–78). Springer. https://doi.org/10.1007/978-3-031-40997-4_5 ( reposiTUm)

Zseby, T. (2013). IP Darkspace Analysis. In Advances in IT Early Warning (pp. 21–29). Fraunhofer IRB Verlag. http://hdl.handle.net/20.500.12708/28077 ( reposiTUm)

Zseby, T. (2024, November 19). AI Strategies for Detecting Malware Communication [Conference Presentation]. 22nd escar Europe - Embedded Security in Cars, Dortmund, Germany. ( reposiTUm)

Zseby, T. (2023, March 31). Features make the difference: How to detect malware communication in network traffic [Presentation]. NET-IT Meeting, online, Austria. ( reposiTUm)

Fabini, J., Hartl, A., Meghdouri, F., & Zseby, T. (2023, October 4). Sicherheit Ladeinfrastruktur eMobilität: Steuerung von Ladeinfrastruktur durch CPOs und Aggregatoren [Conference Presentation]. Oesterreichs Energie E-Mobilitätstage 2023, Wien, Austria. ( reposiTUm)

Zseby, T. (2022, December 9). Malware Communication in Critical Infrastructures [Keynote Presentation]. Dependable Systems, Services and Technologies (DESSERT 2022), Athens, Greece. ( reposiTUm)

Zseby, T. (2022, June 28). Schutz kritischer Infrastruktur: Herausforderungen in Krisenzeiten [Presentation]. Podiumsdiskussion: Technologie und Energieversorgung im Spannungsfeld zwischen Ukraine-Konflikt und Pandemie, Wien, Austria. ( reposiTUm)

Zseby, T. (2022, February 22). Machine Learning for Network Security [Presentation]. AI in Networking Summer School 2022, University of Ulm, Germany. ( reposiTUm)

Hartl, A., Fabini, J., Roschger, C., Eder-Neuhauser, P., Petrovic, M., Tobler, R., & Zseby, T. (2022, March 24). Subverting Counter Mode Encryption for Hidden Communication in High-Security Infrastructures [Conference Presentation]. IETF 113 Meeting - IRTF CFRG, Vienna, Austria. https://doi.org/10.34726/3644 ( reposiTUm)

Zseby, T. (2021). Hidden Malware Communication in Critical Infrastructures. IEEE IAS/PELS/IES Austria Chapter Digital Transformation Technology Webinar (together with Project Sinergy), online, Unknown. http://hdl.handle.net/20.500.12708/91369 ( reposiTUm)

Meghdouri, F., Schmied, T., Gärtner, T., & Zseby, T. (2021). Controllable Network Data Balancing with GANs. NeurIPS workshop on Deep Generative Models and Downstream Applications 2021, Online, Unknown. http://hdl.handle.net/20.500.12708/91382 ( reposiTUm)

Zseby, T. (2021). Detecting Malware Communication: Challenges and Approaches. Vienna CyberSecurity and Privacy Research Cluster: System Security Workshop, Vienna, Austria. http://hdl.handle.net/20.500.12708/91372 ( reposiTUm)

Bachl, M., Hartl, A., Fabini, J., & Zseby, T. (2020). Walling up Backdoors in Intrusion Detection Systems. 1st ITG Workshop on IT Security (ITSec), Tübingen, Germany. http://hdl.handle.net/20.500.12708/91298 ( reposiTUm)

Zseby, T. (2019). Malware Communication: Hiding Techniques and Detection Methods. Ruzena Bajcsy Lectures on Communications, Darmstadt, Germany. http://hdl.handle.net/20.500.12708/91263 ( reposiTUm)

Zseby, T. (2019). (Un-)Reproducible Research: Experiences from Network Traffic Analysis. Networking of Women in Computing, Darmstadt, Germany. http://hdl.handle.net/20.500.12708/91264 ( reposiTUm)

Fabini, J., Xypolytou, E., Zseby, T., Gawlik, W., & Schrödl, M. (2017). The FUSE Microgrid: Academic Research on Critical Infrastructures using the PI System. OsiSoft EMEA Users Conference 2017, London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/90951 ( reposiTUm)

Zseby, T. (2017). Stealthy Communication Methods: Generation and Detection of Covert Channels in TCP/IP Traffic. TU München Informatik-Kolloquium, München, Germany. http://hdl.handle.net/20.500.12708/90962 ( reposiTUm)

Zseby, T. (2017). Using Measurement Data in Network Security Education. Workshop on Active Internet Measurements, San Diego, United States of America (the). http://hdl.handle.net/20.500.12708/90963 ( reposiTUm)

Fabini, J., & Zseby, T. (2016). Delay Measurement Challenges in Mobile Access Networks. Workshop on Active Internet Measurements, San Diego, USA, Non-EU. http://hdl.handle.net/20.500.12708/90753 ( reposiTUm)

Zseby, T. (2016). Anomaly Detection for Network Security. Network Traffic Analysis and Anomaly Detection, Telecommunications Graduate Initiative (TGI) Course, Waterford, Ireland, EU. http://hdl.handle.net/20.500.12708/90693 ( reposiTUm)

Zseby, T. (2016). Covert Communication in Cyber Attacks: How attackers evade detection. CMG-AE IT-Securitytagung, Wien, Austria. http://hdl.handle.net/20.500.12708/90826 ( reposiTUm)

Meisel, M., Wilker, S., Fabini, J., Annessi, R., Zseby, T., Müllner, M., Kastner, W., Litzlbauer, M., Gawlik, W., & Neureiter, C. (2016). Methodical Reference Architecture Development Progress. D-A-CH Energieinformatik Konferenz, Wien, Austria. http://hdl.handle.net/20.500.12708/90885 ( reposiTUm)

Zseby, T. (2014). Feature Selection for Network Security. TU München Informatik-Kolloquium, Technische Universität München, EU. http://hdl.handle.net/20.500.12708/90408 ( reposiTUm)

Zseby, T. (2014). Network Security Lab Exercises with IP Darkspace Data. CAIDA Meeting, University of California San Diego (UCSD), USA, EU. http://hdl.handle.net/20.500.12708/90409 ( reposiTUm)

Zseby, T. (2013). M2M Security Challenges: What is Normal when Machines Communicate? Informationstechnisches Kolloquium “Machine to Machine Communication” 2013, Wien, Austria. http://hdl.handle.net/20.500.12708/90225 ( reposiTUm)

Zseby, T. (2013). Network Security in Smart Grids: Challenges and Opportunities. Workshop on Cryptography and Embedded Security, Embedded World Conference 2013, Nuremberg, Germany, EU. http://hdl.handle.net/20.500.12708/90226 ( reposiTUm)

Zseby, T. (2012). Entropy in IP Darkspace Data. 7th CERT Workshop on Flow Analysis (FloCon 2012), Austin, Texas, USA, Non-EU. http://hdl.handle.net/20.500.12708/90224 ( reposiTUm)

Zseby, T. (2012). Comparable Metrics for IP Darkspace. Analysis. 1st International Workshop on Darkspace and UnSolicited Traffic Analysis (DUST 2012), San Diego, CA, USA, Non-EU. http://hdl.handle.net/20.500.12708/90227 ( 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)

Zseby, T. (2011). Sicherheitsaspekte bei der Nutzung von IP-Technologien in Smart Grids. Fachforum I Datenschutz und Datensicherheit, German Federal Ministry of Economics and Technology (BMWi), Berlin, Germany, EU. http://hdl.handle.net/20.500.12708/90295 ( reposiTUm)

Fabini, J., Hartl, A., Meghdouri, F., & Zseby, T. (2023). Sicherheitsstudie Ladeinfrastrukturanbindung; Steuerung von Ladeinfrastruktur durch CPOs und Aggregatoren. Oesterreichs Energie. http://hdl.handle.net/20.500.12708/189793 ( reposiTUm)

Matz, G., Hlawatsch, F., Schwarz, S., Zseby, T., & Fabini, J. (2024, March). Maschinelles Lernen und Telekommunikation — Eine starke Allianz. Bulletin. Alumni-Magazin der TU Wien, 56, 34–37. http://hdl.handle.net/20.500.12708/206661 ( reposiTUm)