uni.liPersonenverzeichnis

Dr. Giovanni Apruzzese

Wissenschaftlicher Mitarbeiter / Postdoktorand
Hilti Lehrstuhl für Daten- und Anwendungssicherheit
Portrait
Veranstaltungen im WS 20/21
  • 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., Ferretti, L., Marchetti, M., & Colajanni, M. (2021). Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems. Digital Threats: Research and Practice.

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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 (IEEE TETCI), 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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  • 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 The 16th International Conference on Availability, Reliability and Security, Vienna.

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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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