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Staats- und Amtshaftung im Versicherungswesen – oder: Was Mattersburg mit Vaduz (nicht) gemeinsam hat…

Reference

Raschauer, N., Stern, T., Pfisterer, N., & Pfurtscheller, V. (2021). Staats- und Amtshaftung im Versicherungswesen – oder: Was Mattersburg mit Vaduz (nicht) gemeinsam hat…. Liechtensteinische Juristen-Zeitung(2), 90.

Publication Type

Article in Scientific Journal

Staff Members

Prof. Dr. Nicolas Raschauer PD MMag. Dr. Thomas Stern MBA LL.M. (Heidelberg) Mag. iur. Vanessa Pfurtscheller Mag.iur. Nicola Pfisterer

Participating Institutions

Chair for Banking and Financial Market Law Institute for Business Law

Data-driven foresight in life cycle management: an interview study

Reference

Scheuffele, M., Bayrle-Kelso, N., & Brecht, L. (2024). Data-driven foresight in life cycle management: an interview study. In D. Schallmo, A. Baiyere, F. Gersten, C. A. Foss Rosenstand & C. A. Williams (Eds.), Digital Disruption and Transformation - Case Studies, Approaches, and Tools (1 ed., pp. 131-151). Cham: Springer Cham.

Publication Type

Chapter in Edited Book

Staff Members

Prof. Dr. Leo Brecht Dr. Niklas Maximilian Bayrle-Kelso Marie Scheuffele MSc

Participating Institutions

Liechtenstein Business School Entrepreneurship Technology and Innovation

Research

Foresight for a (product) lifetime: developing a lifecycle- encompassing foresight process
FFF-Funding Project
June 2023 until April 2024 (finished)
Dealing with discontinuities in the market, changing competitive advantages, and existing information uncertainties in decision-making processes are daily challenges for companies around the world. ... More

DOI

https://dx.doi.org/10.1007/978-3-031-47888-8_7

Foundation Models

Reference

Schneider, J., Meske, C., & Kuss, P. (2024). Foundation Models. Business & Information Systems Engineering, 66, 221-231.

Publication Type

Article in Scientific Journal

Staff Members

Prof. Dr. Johannes Schneider

Participating Institutions

Liechtenstein Business School Artificial Intelligence and Data Science

Research

Large Language Models - Autograding & Governance for businesses
FFF-Funding Project
September 2023 until July 2025 (finished)
This project aims to investigate the potential of large language models (LLMs), specifically ChatGPT, in two areas: exam grading support and governance of LLMs in an industrial context. The first ... More

DOI

https://dx.doi.org/10.1007/s12599-024-00851-0

SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection

Reference

Apruzzese, G., Laskov, P., & Schneider, J. (2023). SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection. Paper presented at the IEEE European Symposium on Security and Privacy (IEEE EuroS&P), Delft, Netherlands.

Publication Type

Paper in Conference Proceedings

Staff Members

Prof. Dr. Johannes Schneider Prof. Dr. Pavel Laskov Dr. Giovanni Apruzzese

Participating Institutions

Liechtenstein Business School Data and Application Security Artificial Intelligence and Data Science

DOI

https://dx.doi.org/10.1109/eurosp57164.2023.00042

Attacking Logo-based Phishing Website Detectors with Adversarial Perturbations

Reference

Lee, J., Xin, Z., See, M. N., Sabharwal, K., Apruzzese, G., & Divakaran, D. (2023). Attacking Logo-based Phishing Website Detectors with Adversarial Perturbations. Paper presented at the European Symposium on Research in Computer Security (ESORICS).

Publication Type

Paper in Conference Proceedings

Staff Members

Dr. Giovanni Apruzzese

Participating Institutions

Liechtenstein Business School Data and Application Security

DOI

https://dx.doi.org/10.1007/978-3-031-51479-1_9

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act

Understanding the Process of Data Labeling in Cybersecurity

"Do Users fall for Real Adversarial Phishing?" Investigating the Human response to Evasive Webpages

Reference

Draganovic, A., Dambra, S., Aldana louit, J., Roundy, K., & Apruzzese, G. (2023). "Do Users fall for Real Adversarial Phishing?" Investigating the Human response to Evasive Webpages. Paper presented at the APWG Symposium on Electronic Crime Research (eCrime), Barcelona, Spain.

Publication Type

Paper in Conference Proceedings

Staff Members

Dr. Giovanni Apruzzese

Participating Institutions

Liechtenstein Business School Data and Application Security

DOI

https://dx.doi.org/10.1109/ecrime61234.2023.10485552

Dual Adversarial Attacks: Fooling Humans and Classifiers

Multi-SpacePhish: Extending the Evasion Space of Adversarial Attacks against Phishing Website Detectors using Machine Learning

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