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Dr. Giovanni Apruzzese

Assistenzprofessor
Data & Application Security
Portrait
SAMLAF: Security Assessment of Machine Learning Applications in Finance
FFF-Förderprojekt, September 2023 bis Februar 2025

Der algorithmische Handel hat sich zu einem wichtigen Instrument in der Finanzdienstleistungsbranche entwickelt. Die automatische Entscheidungsfindung auf den Finanzmärkten mit Hilfe intelligenter ... mehr

  • 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.

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

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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.

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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.

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  • 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.

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  • 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).

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

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  • 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.

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

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  • 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.

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  • 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).

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  • 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.

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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.

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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).

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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.

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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.

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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.

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  • 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).

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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.

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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., 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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  • 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.

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

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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