22nd Conference on Detection of Intrusions and Malware & Vulnerability Assessment
The SIG SIDAR Conference on Intrusion Detection, Malware, and Vulnerability Assessment (DIMVA) brings together experts and researchers to discuss the latest advancements, challenges, and solutions in cybersecurity. The event focuses on detecting and mitigating intrusions, analyzing malware threats, and assessing system vulnerabilities to enhance digital defense strategies.
Daniel Arp delivered a keynote at DIMVA'25, which was held in Graz from July 9 to 11, 2025.
Title: Lessons Learned in Mobile Malware Detection with Machine Learning
Abstract. Mobile malware continues to pose a serious threat to the security and privacy of mobile device users. In response, the research community has developed a wide range of machine learning-based detection approaches over the past decade, aiming to overcome the limitations of traditional signature-based techniques. While these learning-based methods have demonstrated strong potential, the field still faces a number of unresolved challenges—such as concept drift and evolving adversarial behaviors—that must be addressed to ensure sustained effectiveness in real-world environments. In this talk, we reflect on a decade of research in machine learning-based mobile malware detection, discuss key lessons learned, and highlight ongoing challenges that present opportunities for future work.
