544 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of PaduaIT7 papers
- Delft University of TechnologyNL3 papers
- Hilti (Liechtenstein)LI3 papers
- Liechtenstein InstituteLI2 papers
- Reykjavík UniversityIS2 papers
- Saarland UniversityDE2 papers
- University of Modena and Reggio EmiliaIT2 papers
- Association of Electronic and Information TechnologiesES1 paper
- Berlin Institute for the Foundations of Learning and DataDE1 paper
- BOKU UniversityAT1 paper
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DE1 paper
- École Polytechnique Fédérale de LausanneCH1 paper
27 papers
Propagation Model for SSC attacks: Why SBOM (tools) don't tell the whole truth
Ljubica Grgic, Lazar Maksimovic, Pavel Laskov
Ensuring security of software supply chains (SSC) is indispensable in today's world of modern software practices. SBOM (tools) have been introduced as relevant building blocks to e…
"What is the Problem Space?" Defining Host-space Adversarial Perturbations against Network Intrusion Detection Systems
Miel Verkerken, Laurens D'hooge, Bruno Volckaert +2
Network Intrusion Detection Systems (NIDS) are now increasingly leveraging Machine Learning (ML) techniques to detect malicious network activities. Numerous papers have scrutinized…
SoK: Reshaping Research on Network Intrusion Detection Systems
Giovanni Apruzzese
Network Intrusion Detection Systems (NIDS) have been studied for decades. Hundreds of papers have, e.g., proposed ways to enhance, harden or bypass NIDS. However, the findings of p…
Adversarial News and Lost Profits: Manipulating Headlines in LLM-Driven Algorithmic Trading
Advije Rizvani, Giovanni Apruzzese, Pavel Laskov
Large Language Models (LLMs) are increasingly adopted in the financial domain. Their exceptional capabilities to analyse textual data make them well-suited for inferring the sentim…
It's not Easy: Applying Supervised Machine Learning to Detect Malicious Extensions in the Chrome Web Store
Ben Rosenzweig, Valentino Dalla Valle, Giovanni Apruzzese +1
Google Chrome is the most popular Web browser. Users can customize it with extensions that enhance their browsing experience. The most well-known marketplace of such extensions is…
E-PhishGen: Unlocking Novel Research in Phishing Email Detection
Luca Pajola, Eugenio Caripoti, Stefan Banzer +3
Every day, our inboxes are flooded with unsolicited emails, ranging between annoying spam to more subtle phishing scams. Unfortunately, despite abundant prior efforts proposing sol…
SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithms
Andrea Maldonado, Christian M. M. Frey, Sai Anirudh Aryasomayajula +3
Process mining aims to extract and analyze insights from event logs, yet algorithm metric results vary widely depending on structural event log characteristics. Existing work often…
Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI
Saskia Laura Schröer, Luca Pajola, Alberto Castagnaro +2
As Artificial Intelligence (AI) continues to evolve, it has transitioned from a research-focused discipline to a widely adopted technology, enabling intelligent solutions across va…
The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations
Marie Weinz, Nicola Zannone, Luca Allodi +1
Modern organizations are persistently targeted by phishing emails. Despite advances in detection systems and widespread employee training, attackers continue to innovate, posing on…
The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems powered by Deep Learning
Advije Rizvani, Giovanni Apruzzese, Pavel Laskov
We study the security of stock price forecasting using Deep Learning (DL) in computational finance. Despite abundant prior research on the vulnerability of DL to adversarial pertur…
LLM4PM: A case study on using Large Language Models for Process Modeling in Enterprise Organizations
Clara Ziche, Giovanni Apruzzese
We investigate the potential of using Large Language Models (LLM) to support process model creation in organizational contexts. Specifically, we carry out a case study wherein we d…
"Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages
Ying Yuan, Qingying Hao, Giovanni Apruzzese +2
Machine learning based phishing website detectors (ML-PWD) are a critical part of today's anti-phishing solutions in operation. Unfortunately, ML-PWD are prone to adversarial evasi…
Understanding the Process of Data Labeling in Cybersecurity
Tobias Braun, Irdin Pekaric, Giovanni Apruzzese
Many domains now leverage the benefits of Machine Learning (ML), which promises solutions that can autonomously learn to solve complex tasks by training over some data. Unfortunate…
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
Luca Longo, Mario Brcic, Federico Cabitza +16
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In respo…
Model-Based Generation of Attack-Fault Trees
Raffaela Groner, Thomas Witte, Alexander Raschke +5
Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a co…
Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors
Giovanni Apruzzese, V. S. Subrahmanian
Although machine learning based algorithms have been extensively used for detecting phishing websites, there has been relatively little work on how adversaries may attack such "phi…
Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
Ying Yuan, Giovanni Apruzzese, Mauro Conti
Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model, or defenses that withstand most attacks. Unfortunately, little…
Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2
Pier Paolo Tricomi, Lisa Facciolo, Giovanni Apruzzese +1
Did you know that over 70 million of Dota2 players have their in-game data freely accessible? What if such data is used in malicious ways? This paper is the first to investigate su…
Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples
Giovanni Apruzzese, Rodion Vladimirov, Aliya Tastemirova +1
Fifth Generation (5G) networks must support billions of heterogeneous devices while guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet with hum…
The Role of Machine Learning in Cybersecurity
Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de Oca +4
Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of M…
SoK: The Impact of Unlabelled Data in Cyberthreat Detection
Giovanni Apruzzese, Pavel Laskov, Aliya Tastemirova
Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of sp…
The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
Giovanni Apruzzese, Luca Pajola, Mauro Conti
Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where benign a…
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
Giovanni Apruzzese, Mauro Andreolini, Luca Ferretti +2
The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have…
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection
Andrea Corsini, Shanchieh Jay Yang, Giovanni Apruzzese
Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequen…