Robust Machine Learning for Malware Detection over Time

Understand

The presence and persistence of Android malware is an on-going threat that plagues this information era, and machine learning technologies are now extensively used to deploy more effective detectors that can block the majority of these malicious programs.

• However, these algorithms have not been developed to pursue the natural evolution of malware, and their performances significantly degrade over time because of such concept-drift.
• Currently, state-of-the-art techniques only focus on detecting the presence of such drift, or they address it by relying on frequent updates of models.
• Hence, there is a lack of knowledge regarding the cause of the concept drift, and ad-hoc solutions that can counter the passing of time are still under-investigated.