Innovation
Innovation, as a Topic in Focus for a sustainable society, means exploring new paths to address ecological, social, and economic challenges in the long term. Innovation stands not only for technological progress, but also for new ways of thinking and acting. It forms the foundation of a resilient society, which is shaped through creative approaches, participatory processes, and sustainable business models.
To me, innovation means rethinking financial systems — not just making them more efficient and digital, but also fairer and more sustainable. It opens up the opportunity to create real societal value through creative solutions."
Innovation in Research and Knowledge Transfer
At the University of Liechtenstein, innovation is at the heart of research. As a cross-cutting Topic in Focus, it permeates numerous research areas. The emphasis lies on innovative processes, products, and organisational models that support sustainable development.
Particularly in the field of finance, new approaches are emerging, including digital financial technologies, blockchain, and artificial intelligence, that integrate ecological and social criteria into business models. Interdisciplinary collaboration, practice-oriented research, and a culture of open innovation ensure that theoretical knowledge is transformed into real-world impact.
Innovation means moving forward, being bold and actively shaping the future, as a business, as an individual, as a university or as a political actor. In the short term, innovation is often perceived as uncomfortable because it disrupts existing business models, technological standards and societal structures. In the long run, however, innovation is the engine of our future and driving it forward is, in my view, one of the University of Liechtenstein’s most important responsibilities."
Selected Publications in the Field of Innovation
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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.More
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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).More
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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.More
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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).More
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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).More
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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).More
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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).More
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Schneider, J., & Apruzzese, G. (2023). Dual Adversarial Attacks: Fooling Humans and Classifiers. Journal of Information Security and Applications, 75.More
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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).More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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).More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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.More
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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).More
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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.More
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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.More
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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.More
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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.More
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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.More
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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).More
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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.More