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ORCIDGiovanni Apruzzese
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- affiliation: University of Liechtenstein, Liechtenstein Business School
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2020 – today
- 2024
[c20]Fiona Koh, Kathrin Grosse, Giovanni Apruzzese:
Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act. HICSS 2024: 1870-1879- [c19]Tobias Braun
, Irdin Pekaric, Giovanni Apruzzese:
Understanding the Process of Data Labeling in Cybersecurity. SAC 2024: 1596-1605 - [c18]Qingying Hao, Nirav Diwan, Ying Yuan, Giovanni Apruzzese, Mauro Conti, Gang Wang:
It Doesn't Look Like Anything to Me: Using Diffusion Model to Subvert Visual Phishing Detectors. USENIX Security Symposium 2024 - [c17]Ying Yuan, Qingying Hao, Giovanni Apruzzese, Mauro Conti, Gang Wang:
"Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages. WWW 2024: 1712-1723 - [i19]Ying Yuan, Qingying Hao, Giovanni Apruzzese, Mauro Conti, Gang Wang:
"Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages. CoRR abs/2404.02832 (2024) - [i18]Kevin Lange, Federico Fontana, Francesco Rossi, Mattia Varile, Giovanni Apruzzese:
Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation. CoRR abs/2405.02642 (2024) - 2023
- [j8]Johannes Schneider, Giovanni Apruzzese:
Dual adversarial attacks: Fooling humans and classifiers. J. Inf. Secur. Appl. 75: 103502 (2023) - [j7]Giovanni Apruzzese, V. S. Subrahmanian:
Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors. IEEE Trans. Dependable Secur. Comput. 20(5): 3753-3769 (2023) - [c16]Pier Paolo Tricomi, Lisa Facciolo, Giovanni Apruzzese, Mauro Conti:
Attribute Inference Attacks in Online Multiplayer Video Games: A Case Study on DOTA2. CODASPY 2023: 27-38 - [c15]Ajka Draganovic, Savino Dambra, Javier Aldana-Iuit, Kevin Alejandro Roundy, Giovanni Apruzzese:
"Do Users Fall for Real Adversarial Phishing?" Investigating the Human Response to Evasive Webpages. eCrime 2023: 1-14 - [c14]Jehyun Lee, Zhe Xin, Melanie Ng Pei See, Kanav Sabharwal, Giovanni Apruzzese, Dinil Mon Divakaran:
Attacking Logo-Based Phishing Website Detectors with Adversarial Perturbations. ESORICS (3) 2023: 162-182 - [c13]Giovanni Apruzzese, Pavel Laskov, Johannes Schneider:
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection. EuroS&P 2023: 592-614 - [c12]Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra, David Freeman, Fabio Pierazzi, Kevin A. Roundy:
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice. SaTML 2023: 339-364 - [i17]Giovanni Apruzzese, Pavel Laskov, Johannes Schneider:
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection. CoRR abs/2305.00550 (2023) - [i16]Jehyun Lee, Zhe Xin, Melanie Ng Pei See, Kanav Sabharwal, Giovanni Apruzzese, Dinil Mon Divakaran:
Attacking logo-based phishing website detectors with adversarial perturbations. CoRR abs/2308.09392 (2023) - [i15]Ajka Draganovic, Savino Dambra, Javier Aldana-Iuit, Kevin A. Roundy, Giovanni Apruzzese:
"Do Users fall for Real Adversarial Phishing?" Investigating the Human response to Evasive Webpages. CoRR abs/2311.16383 (2023) - [i14]Tobias Braun, Irdin Pekaric, Giovanni Apruzzese:
Understanding the Process of Data Labeling in Cybersecurity. CoRR abs/2311.16388 (2023) - 2022
- [j6]Giovanni Apruzzese, Luca Pajola, Mauro Conti:
The Cross-Evaluation of Machine Learning-Based Network Intrusion Detection Systems. IEEE Trans. Netw. Serv. Manag. 19(4): 5152-5169 (2022) - [j5]Giovanni Apruzzese, Rodion Vladimirov, Aliya Tastemirova, Pavel Laskov:
Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples. IEEE Trans. Netw. Serv. Manag. 19(4): 5312-5332 (2022) - [c11]Giovanni Apruzzese, Mauro Conti, Ying Yuan:
SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning. ACSAC 2022: 171-185 - [c10]Giovanni Apruzzese, Pavel Laskov, Aliya Tastemirova:
SoK: The Impact of Unlabelled Data in Cyberthreat Detection. EuroS&P 2022: 20-42 - [c9]Johannes Schneider, Giovanni Apruzzese:
Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike. SP (Workshops) 2022: 66-72 - [i13]Giovanni Apruzzese, Luca Pajola, Mauro Conti:
The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems. CoRR abs/2203.04686 (2022) - [i12]Johannes Schneider, Giovanni Apruzzese:
Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike. CoRR abs/2203.10166 (2022) - [i11]Giovanni Apruzzese, Pavel Laskov, Aliya Tastemirova:
SoK: The Impact of Unlabelled Data in Cyberthreat Detection. CoRR abs/2205.08944 (2022) - [i10]Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de Oca, Wissam Mallouli, Luis Burdalo Rapa, Athanasios Vasileios Grammatopoulos, Fabio Di Franco:
The Role of Machine Learning in Cybersecurity. CoRR abs/2206.09707 (2022) - [i9]Giovanni Apruzzese, Rodion Vladimirov, Aliya Tastemirova, Pavel Laskov:
Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples. CoRR abs/2207.01531 (2022) - [i8]Pier Paolo Tricomi, Lisa Facciolo, Giovanni Apruzzese, Mauro Conti:
Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2. CoRR abs/2210.09028 (2022) - [i7]Jacqueline Meyer, Giovanni Apruzzese:
Cybersecurity in the Smart Grid: Practitioners' Perspective. CoRR abs/2210.13119 (2022) - [i6]Giovanni Apruzzese, Mauro Conti, Ying Yuan:
SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning. CoRR abs/2210.13660 (2022) - [i5]Giovanni Apruzzese, V. S. Subrahmanian:
Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors. CoRR abs/2212.05380 (2022) - [i4]Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra, David Freeman, Fabio Pierazzi, Kevin A. Roundy:
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice. CoRR abs/2212.14315 (2022) - 2021
- [c8]Andrea Corsini, Shanchieh Jay Yang, Giovanni Apruzzese:
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection. ARES 2021: 113:1-113:10 - [c7]Martin Husák, Giovanni Apruzzese, Shanchieh Jay Yang, Gordon Werner:
Towards an Efficient Detection of Pivoting Activity. IM 2021: 980-985 - [i3]Andrea Corsini, Shanchieh Jay Yang, Giovanni Apruzzese:
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection. CoRR abs/2106.07961 (2021) - [i2]Giovanni Apruzzese, Mauro Andreolini, Luca Ferretti, Mirco Marchetti, Michele Colajanni:
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems. CoRR abs/2106.09380 (2021) - 2020
- [j4]Giovanni Apruzzese, Mauro Andreolini, Mirco Marchetti, Vincenzo Giuseppe Colacino, Giacomo Russo:
AppCon: Mitigating Evasion Attacks to ML Cyber Detectors. Symmetry 12(4): 653 (2020) - [j3]Giovanni Apruzzese, Fabio Pierazzi, Michele Colajanni, Mirco Marchetti:
Detection and Threat Prioritization of Pivoting Attacks in Large Networks. IEEE Trans. Emerg. Top. Comput. 8(2): 404-415 (2020) - [j2]Giovanni Apruzzese, Mauro Andreolini, Michele Colajanni, Mirco Marchetti:
Hardening Random Forest Cyber Detectors Against Adversarial Attacks. IEEE Trans. Emerg. Top. Comput. Intell. 4(4): 427-439 (2020) - [j1]Giovanni Apruzzese, Mauro Andreolini, Mirco Marchetti, Andrea Venturi, Michele Colajanni:
Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks. IEEE Trans. Netw. Serv. Manag. 17(4): 1975-1987 (2020)
2010 – 2019
- 2019
- [c6]Giovanni Apruzzese, Michele Colajanni, Luca Ferretti, Mirco Marchetti:
Addressing Adversarial Attacks Against Security Systems Based on Machine Learning. CyCon 2019: 1-18 - [c5]Giovanni Apruzzese, Michele Colajanni, Mirco Marchetti:
Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors. NCA 2019: 1-8 - [i1]Giovanni Apruzzese, Mauro Andreolini, Michele Colajanni, Mirco Marchetti:
Hardening Random Forest Cyber Detectors Against Adversarial Attacks. CoRR abs/1912.03790 (2019) - 2018
- [c4]Giovanni Apruzzese, Michele Colajanni, Luca Ferretti, Alessandro Guido, Mirco Marchetti:
On the effectiveness of machine and deep learning for cyber security. CyCon 2018: 371-390 - [c3]Giovanni Apruzzese, Michele Colajanni:
Evading Botnet Detectors Based on Flows and Random Forest with Adversarial Samples. NCA 2018: 1-8 - 2017
- [c2]Fabio Pierazzi, Giovanni Apruzzese, Michele Colajanni, Alessandro Guido, Mirco Marchetti:
Scalable architecture for online prioritisation of cyber threats. CyCon 2017: 1-18 - [c1]Giovanni Apruzzese, Mirco Marchetti, Michele Colajanni, Gabriele Gambigliani Zoccoli, Alessandro Guido:
Identifying malicious hosts involved in periodic communications. NCA 2017: 11-18
Coauthor Index
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last updated on 2024-07-24 21:35 CEST by the dblp team
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