Digitalisierung
Das Kernthema Digitalisierung steht für tiefgreifende gesellschaftliche Veränderungen. Digitale Technologien wie künstliche Intelligenz, Blockchain oder Smart Services prägen neue Formen des Arbeitens, Wirtschaftens und Zusammenlebens. Die differenzierte Auseinanderseztung mit der Digitalisierung berücksichtigt dabei auch soziale und ökologische Auswirkungen.
Digitalisierung bedeutet unter anderem Online-Shopping auf Knopfdruck, sekundenschnelle Antworten auf beliebige Fragen dank „Künstlicher Intelligenz (KI), Videogespräche mit Freunden sowie das Trainieren von KI auf medizinischen Daten, um Leben zu retten. Mein Team arbeitet daran, die Digitalisierung durch greifbare Innovationen voranzutreiben und auch Wissen zu generieren, um Chancen und Risiken besser zu verstehen."
Digitalisierung in Forschung und Transfer
An der Universität Liechtenstein ist Digitalisierung ein zentrales Kernthema in Forschung und Transfer. Der Fokus liegt auf der Entwicklung und Bewertung digitaler Innovationen, die zur nachhaltigen Transformation beitragen. Die Forschung adressiert aktuelle Fragestellungen der digitalen Transformation in Bereichen wie Architektur und Rau,mentwicklung, Wirtschaftswissenschaften, Recht und Gesellschaft. So entstehen neue Lösungsansätze für die Gestaltung einer digitalen und zugleich verantwortungsvollen Zukunft. Im Zentrum stehen anwendungsorientierte Projekte, interdisziplinäre Zusammenarbeit und der direkte Dialog mit Wirtschaft und Gesellschaft.
Digitalisierung prägt zunehmend das Wirtschaftsleben und stellt das Strafrecht vor neue Herausforderungen. Meine Forschung und Lehre umfassen das Wirtschafts- und Cyberstrafrecht sowie aktuelle Regulierungen wie DORA und MiCAR. Zugleich stehen technologische Innovationen wie Blockchain und Künstliche Intelligenz ebenso im Fokus wie deren strafrechtliche und Compliance-relevante Implikationen."
Forschungs- und Transferprojekte im Bereich Digitalisierung
Auswahl unserer Publikationen zu Digitalisierung
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Yuan, Y., Apruzzese, G., & Conti, M. (2025). Beyond the west: Revealing and bridging the gap between Western and Chinese phishing website detection. Computers & Security, 148(January).Weitere
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Schröer, S., Pajola, L., Castagnaro, A., Apruzesse, G., & Conti, M. (2025). Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI. IEEE Intelligent Systems.Weitere
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Peled, A., Leinonen, T., & Hasler, B. S. (2025). Telerobotic Theater of the Oppressed in Israel and Palestine: Becoming Digital Jokers. ACM Transactions on Computer-Human Interaction, 32(3).Weitere
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Rosenzweig, B., Dalla Valle, V., Apruzzese, Giovanni, & Fass, A. (2025). It's Not Easy: Applying Supervised Machine Learning to Detect Malicious Extensions in the Chrome Web Store. ACM Transactions on the Web.Weitere
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Schröer, S., Seideman, J. D., Shoufu, L., Apruzesse, G., Dietrich, S., & Laskov, P. (2025). Using a Stack to Find an AI Needle: Topic Modeling for Cyber Threat Intelligence. Digital Threats: Research and Practice, 6(4).Weitere
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Yuan, Y., Apruzzese, G., & Conti, M. (2024). Multi-SpacePhish: Extending the Evasion Space of Adversarial Attacks against Phishing Website Detectors using Machine Learning. Digital Threats: Research and Practice, 5(2).Weitere
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Pekaric, I., Groner, R., Witte, T., Adigun, J. G., Raschke, A., Federer, M., & Tichy, M. (2023). A systematic review on security and safety of self-adaptive systems. Journal of Systems and Software, 203.Weitere
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Apruzzese, G., & Subrahmanian, V. (2023). Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors. IEEE Transactions on Dependable and Secure Computing (TDSC), 20(5).Weitere
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Schneider, J., & Apruzzese, G. (2023). Dual Adversarial Attacks: Fooling Humans and Classifiers. Journal of Information Security and Applications, 75.Weitere
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Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Burdalo Rapa, L., Grammatopoulos, A. V., & Di Franco, F. (2023). The Role of Machine Learning in Cybersecurity. ACM Digital Threats: Research and Practice, 4(1).Weitere
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Verkerken, M., D'hooge, L., Volckaert, B., De Turck, F., & Apruzzese, G. (2026). ConCap: Practical Network Traffic Generation for (ML- and) Flow-based Intrusion Detection Systems. Paper presented at the 4th IEEE Conference on Secure and Trustworthy Machine Learning, Technical University of Munich, Germany.Weitere
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Pajola, L., Caripoti, E., Banzer, S., Pizzi, S., Conti, M., & Apruzzese, G. (2025). E-PhishGEN: Unlocking Novel Research in Phishing Email Detection. Paper presented at the 18 th ACM Workshop on Artificial Intelligence and Security, Taipei, Taiwan.Weitere
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Weinz, M., Zannone, N., Allodi, L., & Apruzesse, G. (2025). The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations. Paper presented at the 20th ACM Asia Conference on Computer and Communications Security.Weitere
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Flores Comeca, A. L., Masarykova, N., Halinkovic, M., Galinski, M., Laskov, P., & Vinel, A. (2025). Social Robots for Road Safety: Pedestrian Crossing Assistance Use-Case. Paper presented at the International Symposium ELMAR.Weitere
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Pfister, M., Arpuzzese, G., & Pekaric, I. (2025). Department-Specific Security Awareness Campaigns: A Cross-Organizational Study of HR and Accounting. Paper presented at the APWG's 2025 Symposium on Electronic Crime Research (eCrime 2025), San Diego, USA.Weitere
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Vladimirov, R., & Laskov, P. (2025). The Risk of Adversarial Perturbations for Deep Learning in Antenna Measurements. Paper presented at the 2025 IEEE Conference on Antenna Measurements and Applications (CAMA), Antibes Juan-les-Pins, France.Weitere
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Pekaric, I., & Apruzzese, G. (2025). "We provide our resources in a dedicated repository": Surveying the Transparency of HICSS Publications. Paper presented at the 58th Hawaii International Conference on System Sciences.Weitere
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Vladimirov, R., & Laskov, P. (2025). Challenges of Predictive Beamforming Using Geographical Positioning: Insights from the DeepSense Dataset. Paper presented at the 16th edition of the IFIP Wireless and Mobile Networking Conference (IFIP WMNC), Leuven, Belgium.Weitere
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Schröer, S., Apruzzese, G., Human, S., Laskov, P., Anderson, H., Bernroider, E., Fass, A., Nassi, B., Rimmer, V., Roli, F., Salam, S., Sehn, A., Sunyaev, A., Wadhaw-Brown, T., Wagner, I., & Wang, G. (2025). SoK: On the Offensive Potential of AI. Paper presented at the 3rd IEEE Conference on Secure and Trustworthy Machine Learning, Copenhagen, Denmark.Weitere
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Rizvani, A., Apruzzese, G., & Laskov, P. (2025). The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems Powered by Deep Learning. Paper presented at the 15th ACM Conference on Data and Application Security and Privacy, Pittsburgh, USA.Weitere
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Pekaric, I., Sauerwein, C., Laichner, S., & Breu, R. (2025). How Do Mobile Applications Enhance Security? An Exploratory Analysis of Use Cases and Provided Information. Paper presented at the 2025 ACM Southeast Conference, Cape Girardeau, Missouri.Weitere
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Pajola, L., Schröer, S. L., Tricomi, P. P., Conti, M., & Apruzzese, G. (2025). Elephant in the Room: Dissecting and Reflecting on the Evolution of Online Social Network Research. Paper presented at the Nineteenth International AAAI Conference on Web and Social Media, Copenhagen, Denmark.Weitere
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Schröer, S., Canevascini, N., Pekaric, I., Widmer, P., & Laskov P. (2025). The Dark Side of the Web: Towards Understanding Various Data Sources in Cyber Threat Intelligence. Paper presented at the 7th Workshop on Attackers and Cyber-Crime Operations, IEEE European Symposium on Security and Privacy, Venezia, Italy.Weitere
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Flores Comeca, A. L., Masarykova, N., Halinkovic, M., Galinski, M., Laskov, P., & Vinel, A. (2025). Robots for Safer Pedestrian Crossing on Two-Lane Roads. Paper presented at the 2025 IEEE International Automated Vehicle Validation Conference (IAVVC).Weitere
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Koh, F., Grosse, K., & Apruzzese, G. (2024). Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act. Paper presented at the Hawaii International Conference on System Sciences (HICSS).Weitere
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Lange, K., Fontana, F., Rossi, F., Varile, M., & Apruzzese, G. (2024). Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation. Paper presented at the IEEE Space Computing Conference, Mountain Vies, USA.Weitere
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Weinz, M., Schröer, S. L., & Apruzzese, G. (2024). "Hey Google, Remind me to be Phished" Exploiting the Notificatons of the Google (AI) Assistant on Android for Social Engineering Attacks. Paper presented at the APWG Symposium on Electronic Crime Research, Boston, USA.Weitere
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Apruzzese, G., Fass, A., & Pierrazzi, F. (2024). When Adversarial Perturbations meet Concept Drift: An Exploratory Analysis on ML-NIDS. Paper presented at the 2024 Workshop on Artifical Intelligence and Security.Weitere
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Eisele, L., & Apruzzese, G. (2024). “Hey Players, there is a problem…”: On Attribute Inference Attacks against Videogamers. Paper presented at the IEEE Conference on Games, Milan, Italy.Weitere
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Hao, Q., Diwan, N., Yuan, Y., Apruzzese, G., Conti, M., & Wang, G. (2024). It Doesn’t Look Like Anything to Me: Using Diffusion Model to Subvert Visual Phishing Detectors. Paper presented at the 33rd USENIX Security Symposium, Philadelphia, USA.Weitere
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Ziche, C., & Apruzzese, G. (2024). LLM4PM: A Case Study on Using Large Language Models for Process Modeling in Enterprise Organizations. Paper presented at the International Conference on Business Process Management, Krakow, Poland.Weitere
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Yuan, Y., Hao, Q., Apruzzese, G., Conti, M., & Wang, G. (2024). "Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages. Paper presented at the ACM Web Conference 2024, Singapore.Weitere
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Eisele, L., & Apruzzese, G. (2024). "Are Crowdsourcing Platforms Reliable for Video Game-related Research?" A Case Study on Amazon Mechanical Turk. Paper presented at the 2014 Annual Symposium on Computer-Human Interaction in Play, Tampere, Finland.Weitere
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Pekaric, I., Frick, M., Adigun, J. G., Groner, R., Witte, T., Raschke, A., Felderer, M., & Tichy, M. (2024). Streamlining Attack Tree Generation: A Fragment-Based Approach. Paper presented at the 57th Hawaii International Conference on System Sciences.Weitere
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Braun, T., Pekaric, I., & Apruzzese, G. (2024). Understanding the Process of Data Labeling in Cybersecurity. Paper presented at the ACM Symposium on Applied Computing (ACM SAC), Avila, Spain.Weitere
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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).Weitere
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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.Weitere
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Apruzzese, G., Anderson, H. S., Dambra, S., Freeman, D., Pierazzi, F., & Roundy, K. A. (2023). "Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice. Paper presented at the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Raleigh, North Carolina, USA.Weitere
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Tricomi, P. P., Facciolo, L., Apruzzese, G., & Conti, M. (2023). Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2. Paper presented at the ACM Conference on Data and Application Security and Privacy (CODASPY), Charlotte, NC, United States.Weitere
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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.Weitere
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Groner, R., Witte, T., Raschke, A., Hirn, S., Pekaric, I., Frick, M., Tichy, M., & Felderer, M. (2023). Model-Based Generation of Attack-Fault Trees. Paper presented at the International Conference on Computer Safety, Reliability, and Security.Weitere