Seguir
Giovanni Apruzzese
Título
Citado por
Citado por
Año
On the Effectiveness of Machine and Deep Learning for Cyber Security
G Apruzzese, M Colajanni, L Ferretti, A Guido, M Marchetti
International Conference on Cyber Conflict (CyCon), 371-390, 2018
3602018
Addressing Adversarial Attacks Against Security Systems based on Machine Learning
G Apruzzese, M Colajanni, L Ferretti, M Marchetti
International Conference on Cyber Conflict (CyCon) 900, 1-18, 2019
1172019
Deep Reinforcement Adversarial Learning against Botnet Evasion Attacks
G Apruzzese, M Andreolini, M Marchetti, A Venturi, M Colajanni
IEEE Transactions on Network and Service Management 17 (4), 1975-1987, 2020
952020
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
G Apruzzese, M Andreolini, L Ferretti, M Marchetti, M Colajanni
ACM Digital Threats: Research and Practice, 2021
912021
The Role of Machine Learning in Cybersecurity
G Apruzzese, P Laskov, EM de Oca, W Mallouli, LB Rapa, ...
ACM Digital Threats: Research and Practice, 2022
882022
“Real Attackers Don’t Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice
G Apruzzese, HS Anderson, S Dambra, D Freeman, F Pierazzi, ...
First IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023
622023
Evading Botnet Detectors based on Flows and Random Forest with Adversarial Samples
G Apruzzese, M Colajanni
[Best Student Paper Award] 2018 IEEE 17th International Symposium on Network …, 2018
592018
The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
G Apruzzese, L Pajola, M Conti
IEEE Transactions on Network and Service Management, 2022
572022
Hardening Random Forest Cyber Detectors against Adversarial Attacks
G Apruzzese, M Andreolini, M Colajanni, M Marchetti
IEEE Transactions on Emerging Topics in Computational Intelligence 4 (4 …, 2020
532020
Evaluating the Effectiveness of Adversarial Attacks against Botnet Detectors
G Apruzzese, M Colajanni, M Marchetti
[Best Student Paper Award] International Symposium on Network Computing and …, 2019
452019
Detection and Threat Prioritization of Pivoting Attacks in Large Networks
G Apruzzese, F Pierazzi, M Colajanni, M Marchetti
IEEE Transactions on Emerging Topics in Computing 8 (2), 404-415, 2017
412017
SoK: The Impact of Unlabelled Data in Cyberthreat Detection
G Apruzzese, P Laskov, A Tastemirova
[Oustanding Presentation Award] IEEE European Symposium on Security and …, 2022
272022
DReLAB–Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems
A Venturi, G Apruzzese, M Andreolini, M Colajanni, M Marchetti
Data in Brief, 106631, 2020
272020
Identifying Malicious Hosts involved in Periodic Communications
G Apruzzese, M Marchetti, M Colajanni, GG Zoccoli, A Guido
International Symposium on Network Computing and Applications (NCA), 1-8, 2017
272017
Scalable Architecture for Online Prioritisation of Cyber Threats
F Pierazzi, G Apruzzese, M Colajanni, A Guido, M Marchetti
International Conference on Cyber Conflict (CyCon), 1-18, 2017
262017
AppCon: Mitigating Evasion Attacks to ML Cyber Detectors
G Apruzzese, M Andreolini, M Marchetti, VG Colacino, G Russo
Symmetry 12 (4), 653, 2020
192020
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection
A Corsini, SJ Yang, G Apruzzese
International Conference on Availability, Reliability and Security (ARES …, 2021
162021
SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
G Apruzzese, M Conti, Y Yuan
Annual Computer Security Applications Conference (ACSAC) [Artifact: Reusable …, 2022
152022
Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors
G Apruzzese, VS Subrahmanian
IEEE Transactions on Dependable and Secure Computing (TDSC), 2022
122022
Towards an Efficient Detection of Pivoting Activity
M Husák, G Apruzzese, SJ Yang, G Werner
IFIP/IEEE International Symposium on Integrated Network Management (IM), 980 …, 2021
122021
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20