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Hilti Chair for Data and Application Security

Team

Detection of illicit cryptomining using network metadata

Goupil, F., Laskov, P., Pearic, I., Felderer, M., Duerr, A., & Thiesse, F. (2022). Understanding the Skil Gap in Cybersecurity. Paper presented at the Proceedings of 27th Annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE).
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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).
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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).
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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).
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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, 19(4).
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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, 3(3).
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On the Evaluation of Sequential Machine Learning for Network Intrusion Detection

Reference

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.

Publication Type

Paper in Conference Proceedings

Staff Members

Dr. Giovanni Apruzzese

Participating Institutions

Institute of Information Systems Hilti Chair for Data and Application Security

DOI

https://dx.doi.org/10.1145/3465481.3470065

Bringing order to approximate matching: Classification and attacks on similarity digest algorithms

Towards an Efficient Detection of Pivoting Activity

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