Scalable architecture for online prioritisation of cyber threats. - Fabio Pierazzi; Giovanni Apruzzese; Michele Colajanni; Alessandro Guido; Mirco Marchetti (CyCon, 2017)

Identifying malicious hosts involved in periodic communications. - Giovanni Apruzzese; Mirco Marchetti; Michele Colajanni; Gabriele Gambigliani Zoccoli; Alessandro Guido (NCA, 2017)

On the effectiveness of machine and deep learning for cyber security. - Giovanni Apruzzese; Michele Colajanni; Luca Ferretti; Alessandro Guido; Mirco Marchetti (CyCon, 2018)

Evading Botnet Detectors Based on Flows and Random Forest with Adversarial Samples. - Giovanni Apruzzese; Michele Colajanni (NCA, 2018)

Addressing Adversarial Attacks Against Security Systems Based on Machine Learning. - Giovanni Apruzzese; Michele Colajanni; Luca Ferretti; Mirco Marchetti (CyCon, 2019)

Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors. - Giovanni Apruzzese; Michele Colajanni; Mirco Marchetti (NCA, 2019)

AppCon: Mitigating Evasion Attacks to ML Cyber Detectors. - Giovanni Apruzzese; Mauro Andreolini; Mirco Marchetti; Vincenzo Giuseppe Colacino; Giacomo Russo (Symmetry, 2020)

Detection and Threat Prioritization of Pivoting Attacks in Large Networks. - Giovanni Apruzzese; Fabio Pierazzi; Michele Colajanni; Mirco Marchetti (IEEE Trans. Emerg. Top. Comput., 2020)

Hardening Random Forest Cyber Detectors Against Adversarial Attacks. - Giovanni Apruzzese; Mauro Andreolini; Michele Colajanni; Mirco Marchetti (IEEE Trans. Emerg. Top. Comput. Intell., 2020)

Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks. - Giovanni Apruzzese; Mauro Andreolini; Mirco Marchetti; Andrea Venturi; Michele Colajanni (IEEE Trans. Netw. Serv. Manag., 2020)

On the Evaluation of Sequential Machine Learning for Network Intrusion Detection. - Andrea Corsini; Shanchieh Jay Yang; Giovanni Apruzzese (ARES, 2021)

Towards an Efficient Detection of Pivoting Activity. - Martin Husák; Giovanni Apruzzese; Shanchieh Jay Yang; Gordon Werner (IM, 2021)

SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning. - Giovanni Apruzzese; Mauro Conti; Ying Yuan 002 (ACSAC, 2022)

SoK: The Impact of Unlabelled Data in Cyberthreat Detection. - Giovanni Apruzzese; Pavel Laskov; Aliya Tastemirova (EuroS&P, 2022)

Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike. - Johannes Schneider 002; Giovanni Apruzzese (SP (Workshops), 2022)

Cybersecurity in the Smart Grid: Practitioners' Perspective. - Jacqueline Meyer; Giovanni Apruzzese (CoRR, 2022)

Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems. - Giovanni Apruzzese; Mauro Andreolini; Luca Ferretti; Mirco Marchetti; Michele Colajanni (DTRAP, 2022)

The Cross-Evaluation of Machine Learning-Based Network Intrusion Detection Systems. - Giovanni Apruzzese; Luca Pajola; Mauro Conti (IEEE Trans. Netw. Serv. Manag., 2022)

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples. - Giovanni Apruzzese; Rodion Vladimirov; Aliya Tastemirova; Pavel Laskov (IEEE Trans. Netw. Serv. Manag., 2022)

Attribute Inference Attacks in Online Multiplayer Video Games: A Case Study on DOTA2. - Pier Paolo Tricomi; Lisa Facciolo; Giovanni Apruzzese; Mauro Conti (CODASPY, 2023)

"Do Users Fall for Real Adversarial Phishing?" Investigating the Human Response to Evasive Webpages. - Ajka Draganovic; Savino Dambra; Javier Aldana-Iuit; Kevin Alejandro Roundy; Giovanni Apruzzese (eCrime, 2023)

Attacking Logo-Based Phishing Website Detectors with Adversarial Perturbations. - Jehyun Lee; Zhe Xin; Melanie Ng Pei See; Kanav Sabharwal; Giovanni Apruzzese; Dinil Mon Divakaran (ESORICS (3), 2023)

SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection. - Giovanni Apruzzese; Pavel Laskov; Johannes Schneider 002 (EuroS&P, 2023)

"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice. - Giovanni Apruzzese; Hyrum S. Anderson; Savino Dambra; David Freeman; Fabio Pierazzi; Kevin A. Roundy (SaTML, 2023)

The Role of Machine Learning in Cybersecurity. - Giovanni Apruzzese; Pavel Laskov; Edgardo Montes de Oca; Wissam Mallouli; Luis Burdalo Rapa; Athanasios Vasileios Grammatopoulos; Fabio Di Franco (DTRAP, 2023)

Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors. - Giovanni Apruzzese; V. S. Subrahmanian (IEEE Trans. Dependable Secur. Comput., 2023)

Dual adversarial attacks: Fooling humans and classifiers. - Johannes Schneider 002; Giovanni Apruzzese (J. Inf. Secur. Appl., 2023)

When Adversarial Perturbations meet Concept Drift: An Exploratory Analysis on ML-NIDS. - Giovanni Apruzzese; Aurore Fass; Fabio Pierazzi (AISec@CCS, 2024)

LLM4PM: A Case Study on Using Large Language Models for Process Modeling in Enterprise Organizations. - Clara Ziche; Giovanni Apruzzese (BPM (Blockchain and RPA Forum), 2024)

"Are Crowdsourcing Platforms Reliable for Video Game-related Research?" A Case Study on Amazon Mechanical Turk. - Linus Eisele; Giovanni Apruzzese (CHI PLAY (Companion), 2024)

"Hey Players, there is a problem...": On Attribute Inference Attacks against Videogamers. - Linus Eisele; Giovanni Apruzzese (CoG, 2024)

"Hey Google, Remind Me to Be Phished" Exploiting the Notifications of the Google (AI) Assistant on Android for Social Engineering Attacks. - Marie Weinz; Saskia Laura Schröer; Giovanni Apruzzese (eCrime, 2024)

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act. - Fiona Koh; Kathrin Grosse; Giovanni Apruzzese (HICSS, 2024)

Understanding the Process of Data Labeling in Cybersecurity. - Tobias Braun; Irdin Pekaric; Giovanni Apruzzese (SAC, 2024)

It Doesn't Look Like Anything to Me: Using Diffusion Model to Subvert Visual Phishing Detectors. - Qingying Hao; Nirav Diwan; Ying Yuan 002; Giovanni Apruzzese; Mauro Conti; Gang Wang 011 (USENIX Security Symposium, 2024)

"Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages. - Ying Yuan 002; Qingying Hao; Giovanni Apruzzese; Mauro Conti; Gang Wang 011 (WWW, 2024)

Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation. - Kevin Lange; Federico Fontana; Francesco Rossi; Mattia Varile; Giovanni Apruzzese (CoRR, 2024)

Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors Using Machine Learning. - Ying Yuan 002; Giovanni Apruzzese; Mauro Conti (DTRAP, 2024)

The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems powered by Deep Learning. - Advije Rizvani; Giovanni Apruzzese; Pavel Laskov (CODASPY, 2025)

Elephant in the Room: Dissecting and Reflecting on the Evolution of Online Social Network Research. - Luca Pajola; Saskia Laura Schröer; Pier Paolo Tricomi; Mauro Conti; Giovanni Apruzzese (ICWSM, 2025)

SoK: On the Offensive Potential of AI. - Saskia Laura Schröer; Giovanni Apruzzese; Soheil Human; Pavel Laskov; Hyrum S. Anderson; Edward W. N. Bernroider; Aurore Fass; Ben Nassi; Vera Rimmer; Fabio Roli; Samer Salam; Chi En Ashley Shen; et al. (SaTML, 2025)

Beyond the west: Revealing and bridging the gap between Western and Chinese phishing website detection. - Ying Yuan 002; Giovanni Apruzzese; Mauro Conti (Comput. Secur., 2025)

The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations. - Marie Weinz; Nicola Zannone; Luca Allodi; Giovanni Apruzzese (CoRR, 2025)

Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI. - Saskia Laura Schröer; Luca Pajola; Alberto Castagnaro; Giovanni Apruzzese; Mauro Conti (CoRR, 2025)