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
5 papers · 2 filters
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…
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…