Poster: Scanning-Free Personalized Malware Warning System By Learning Implicit Feedback From Detection Logs

Nowadays, World Wide Web connects people to each other in many ways ubiquitously. Followed along with the convenience and usability, millions of malware infect various devices of numerous users through the web every day. In contrast, traditional anti-malware systems detect such malware by scanning file systems and provide secure environments for users. However, some malware might not be detected by traditional scanning-based detection systems due to hackers' obfuscation techniques. Also, scanning-based approaches cannot caution users for uninfected malware with high risks. In this paper, we aim to build a personalized malware warning system. Different from traditional scanning-based approaches, we focus on discovering the potential malware which has not been detected for each user. If users and the system know the potentially infected malware in advance, they can be alert against the corresponding risks. We propose a novel approach to learn the implicit feedback from detection logs and give a personalized risk ranking of malware for each user. Finally, the experiments on real-world detection datasets demonstrate the proposed algorithm outperforms traditional popularity-based algorithms.