Mobile malware isn’t going away — it’s getting smarter. Over the past decade, researchers have turned to machine learning to detect these threats, but how well do these methods really hold up? In this talk, TU Wien Professor Daniel Arp draws on ten years of cutting-edge research to unpack why traditional detection falls short, how machine learning models adapt (or fail to), and what challenges like concept drift and adversarial evasion still stand in the way of truly robust, real-world defenses.

📍 Location: TU Wien - FAV 02 (https://maps.tuwien.ac.at/?q=HEEG03)

📅 Date: Wednesday, June 18th 2025

🕖 Time: 18:00–20:00

🍕 Perks? Free pizza, snacks & drinks, great people, top-tier AI content!

💸 Cost: Free! Limited capacity – please register early to secure your spot!

🎙 Meet our speaker:

Daniel Arp is a tenure-track Assistant Professor at TU Wien's Security and Privacy Research Unit. He previously held research positions at TU Berlin, University College London, and King’s College London. He earned his Ph.D. with honors from TU Braunschweig and holds a Master’s in Computer Engineering from TU Berlin. His research focuses on developing learning-based systems for detecting and profiling cyber threats like malware, but also on ensuring the robustness and security of these novel systems themselves.