Dr. Giovanni Apruzzese

Yuan, Y., Apruzzese, G., & Conti, M. (2023). Multi-SpacePhish: Extending the Evasion Space of Adversarial Attacks against Phishing Website Detectors using Machine Learning. Digital Threats: Research and Practice.

Schneider, J., & Apruzzese, G. (2023). Dual Adversarial Attacks: Fooling Humans and Classifiers. Journal of Information Security and Applications, 75.

Apruzzese, G., Andreolini, M., Ferretti, L., Marchetti, M., & Colajanni, M. (2022). Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems. Digital Threats: Research and Practice.

Apruzzese, G., Pajola, L., & Conti, M. (2022). The Cross-Evaluation of Machine Learning-based Network Intrusion Detection Systems. IEEE Transactions on Network and Service Management.

Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Burdalo Rapa, L., Grammatopoulos, A. V., & Di Franco, F. (2022). The Role of Machine Learning in Cybersecurity. ACM Digital Threats: Research and Practice.

Apruzzese, G., Vladimirov, R., Tastemirova, A., & Laskov, P. (2022). Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples. IEEE Transactions on Network and Service Management (TNSM).

Apruzzese, G., & Subrahmanian, V. (2022). Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors. IEEE Transactions on Dependable and Secure Computing (TDSC).

Venturi, A., Apruzzese, G., Andreolini, M., Colajanni, M., & Marchetti, M. (2021). DReLAB–Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems. Data in Brief, 34, 106631.

Apruzzese, G., Andreolini, M., Marchetti, M., Colacino, V. G., & Russo, G. (2020). AppCon: Mitigating Evasion Attacks to ML Cyber Detectors. Symmetry, 12(4), 653.

Apruzzese, G., Andreolini, M., Colajanni, M., & Marchetti, M. (2020). Hardening Random Forest Cyber Detectors Against Adversarial Attacks. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(4), 427-439.

Apruzzese, G., Andreolini, M., Marchetti, M., Venturi, A., & Colajanni, M. (2020). Deep Reinforcement Adversarial Learning against Botnet Evasion Attacks. IEEE Transactions on Network and Service Management, 17(4).

Apruzzese, G., Pierazzi, F., Colajanni, M., & Marchetti, M. (2017). Detection and threat prioritization of pivoting attacks in large networks. IEEE Transactions on Emerging Topics in Computing (IEEE TETC), 8(2), 404-415.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

Lee, J., Xin, Z., Pei See, M., 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).

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.

Schneider, J., & Apruzzese, G. (2022). Concept-based Adversarial Attacks: Tricking Classifiers and Humans alike. Paper presented at the IEEE Symposium on Security and Privacy: Deep Learning and Security Workshop (SP DLS).

Apruzzese, G., Tastemirova, A., & Laskov, P. (2022). SoK: The Impact of Unlabelled Data for Cyberthreat Detection. Paper presented at the IEEE European Symposium on Security and Privacy (EuroSP).

Apruzzese, G., Conti, M., & Yuan, Y. (2022). SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning. Paper presented at the Annual Computer Security Applications Conference, Austin, Texas, USA.

Meyer, J., & Apruzzese, G. (2022). Cybersecurity in the Smart Grid: Practitioners' Perspective. Paper presented at the Industrial Control Systems Security Workshop (ICSS).

Husák, M., Apruzzese, G., Yang, S. J., & Werner, G. (2021). Towards an Efficient Detection of Pivoting Activity. Paper presented at the 17th IFIP/IEEE International Symposium on Integrated Network Management - GraSec Workshop, Bordeaux, France.

Corsini, A., Yang, S. J., & Apruzzese, G. (2021). On the Evaluation of Sequential Machine Learning for Network Intrusion Detection. Paper presented at the 16th International Conference on Availability, Reliability and Security, Vienna, Austria.

Apruzzese, G., Colajanni, M., Ferretti, L., & Marchetti, M. (2019). Addressing adversarial attacks against security systems based on machine learning. Paper presented at the 11th International Conference on Cyber Conflict (CyCon), Tallinn, Estonia.

Apruzzese, G., Colajanni, M., & Marchetti, M. (2019). Evaluating the effectiveness of adversarial attacks against botnet detectors. Paper presented at the IEEE 18th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.

Apruzzese, G., & Colajanni, M. (2018). Evading botnet detectors based on flows and Random Forest with adversarial samples. Paper presented at the 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.

Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., & Marchetti, M. (2018). On the effectiveness of machine and deep learning for cyber security. Paper presented at the 10th International Conference on Cyber Conflict (CyCon), Tallinn, Estonia.

Pierazzi, F., Apruzzese, G., Colajanni, M., Guido, A., & Marchetti, M. (2017). Scalable architecture for online prioritisation of cyber threats. Paper presented at the 9th International Conference on Cyber Conflict (CyCon), Tallinn, Estonia.

Apruzzese, G., Marchetti, M., Colajanni, M., Gambigliani Zoccoli, G., & Guido, A. (2017). Identifying malicious hosts involved in periodic communications. Paper presented at the IEEE 16th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.