Publikationen: Institut für IT Sicherheitsforschung – FHSTP

Stöger, K., Schneeberger, D., Kieseberg, P., & Holzinger, A. (2021). Legal aspects of data cleansing in medical AI. Computer Law & Security Review, 42. https://doi.org/https://doi.org/10.1016/j.clsr.2021.105587

Holzinger, A., Kieseberg, P., Tjoa, A. M., & Weippl, E. (2020). Machine Learning and Knowledge Extraction: Fourth IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2020. Springer. https://link.springer.com/book/10.1007/978-3-030-57321-8

Holzinger, A., Kieseberg, P., & Müller, H. (2020). KANDINSKY Patterns: A Swiss-Knife for the Study of Explainable AI. ERCIM-News, 120, 41–42. https://phaidra.fhstp.ac.at/o:4336

Longo, L., Goebel, R., Lecue, F., Kieseberg, P., & Holzinger, A. (2020, August 27). Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions. International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Virtuell.

Bibtex

Pellegrini, T., Blomqvist, E., Groth, P., de Boer, V., Alam, M., Käfer, T., Kieseberg, P., Kirrane, S., Meroño-Peñuela, A., & Pandit, H. J. (Eds.). (2020). Semantic Systems. In the Era of Knowledge Graphs: 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7–10, 2020, Proceedings (Vol. 12378). Springer International Publishing. https://doi.org/10.1007/978-3-030-59833-4

Schacht, B., & Kieseberg, P. (2020). An Analysis of 5 Million OpenPGP Keys. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 11(3), 107–140. http://isyou.info/jowua/papers/jowua-v11n3-6.pdf

Amiri, F., Quirchmayr, G., Kieseberg, P., Bertone, A., & Weippl, E. (2019). Efficiently Vectorized Anonymization in Data Mining using Genetic Algorithms. Proceedings of the 34th International Conference on ICT Systems Security and Privacy Protection-IFIP SEC 2019.

Bibtex

Holzinger, A., Kieseberg, P., Tjoa, A. M., & Weippl, E. (2019). Machine Learning and Knowledge Extraction: Third IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2019. Springer. https://link.springer.com/book/10.1007/978-3-030-29726-8

Kreimel, P., & Tavolato, P. (2019, December 9). Neural Net-Based Anomaly Detection System in Substation Networks. 6th International Symposium for ICS & SCADA Cyber Security Research, Athen, Griechenland.

Bibtex

Luh, R., & Schrittwieser, S. (2019). Advanced threat intelligence: detection and classification of anomalous behavior in system processes. E \& i Elektrotechnik Und Informationstechnik, Springer, 1–7.

Bibtex

Luh, R., Janicke, H., & Schrittwieser, S. (2019). AIDIS: Detecting and classifying anomalous behavior in ubiquitous kernel processes. Computers & Security, 84, 120–147. https://doi.org/10/gh38cc

Neumaier, S., & Polleres, A. (2019). Enabling Spatio-Temporal Search in Open Data. Journal of Web Semantics, 55(Elsevier), 21–36. https://doi.org/10/ggwb56

Ruotsalainen, H., Zhang, J., & Grebeniuk, S. (2019). Experimental Investigation on Wireless Key Generation for Low Power Wide Area Networks. IEEE Internet of Things Journal. https://doi.org/10/ggxwns

Bibtex

Tavolato-Wötzl, C., & Tavolato, P. (2019, February). Analytical Modelling of Cyber-Physical Systems. Proceedings of the 5th International Conference on Information Systems Security and Privacy - ICISSP 2019, 3rd International Workshop on FORmal Methods for Security Engineering - ForSE 2019.

Bibtex

Wenzl, M., Merzdovnik, G., Ullrich, J., & Weippl, E. (2019). From Hack to Elaborate Technique—A Survey on Binary Rewriting. ACM Computing Surveys, 52(3 / Artikel 49). https://doi.org/10.1145/3316415

Amiri, F., Quirchmayr, G., & Kieseberg, P. (2018). A Machine Learning Approach for Privacy-preservation in E-business Applications: Proceedings of the 15th International Joint Conference on E-Business and Telecommunications, 443–452. https://doi.org/10/gh38cd

Holzinger, A., Kieseberg, P., Weippl, E., & Tjoa, A. M. (2018). Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI. In A. Holzinger, P. Kieseberg, A. M. Tjoa, & E. Weippl (Eds.), Machine Learning and Knowledge Extraction (Vol. 11015, pp. 1–8). Springer International Publishing. https://doi.org/10.1007/978-3-319-99740-7_1

Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., Kieseberg, P., & Holzinger, A. (2018). Explainable AI: The New 42? In A. Holzinger, P. Kieseberg, A. M. Tjoa, & E. Weippl (Eds.), Machine Learning and Knowledge Extraction (Vol. 11015, pp. 295–303). Springer International Publishing. https://doi.org/10.1007/978-3-319-99740-7_21

CD-MAKE. (2018). Machine learning and knowledge extraction: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12.9, International Cross-Domain Conference, CD-MAKE 2018, Hamburg, Germany, August 27–30, 2018: proceedings (A. Holzinger, P. Kieseberg, A. M. Tjoa, & E. R. Weippl, Eds.). Springer.

Bibtex

Kieseberg, P., Schrittwieser, S., & Weippl, E. (2018). Structural Limitations of B+-Tree forensics. Proceedings of the Central European Cybersecurity Conference 2018 on - CECC 2018, 1–4. https://doi.org/10/gh372c