1. Read the competition rules.

2. Download the datasets.

3. Implement your detector (follow the instructions here).

4. Run the provided code and submit the results here.

Deadline: March 31, 2025 at 23:59 AoE.

The Robust Android Malware Detection Competition, linked to the Cybersecurity Use Case of ELSA EU project, aims to evaluate machine learning methods when they are used as a first line of defense against malicious software (malware) targeting the Android Operating System. On this task, machine learning usually performs well, learning common patterns from data and enabling the detection of potentially never-before-seen malware samples. However, it has been shown that those detectors (i) tend to exhibit a rapid performance decay over time due to the natural evolution of samples and (ii) can be bypassed by slightly manipulating malware samples in an adversarial manner. The practical impact of these two issues is that current learning-based malware detectors need constant updates and retraining on newly collected and labeled data.

We propose a threefold benchmark to provide tools for comparing AI-based Android malware detectors in a realistic setting. They challenge the research community to go beyond simplistic assumptions to ultimately design more robust AI models that can be maintained and updated more efficiently, saving human labor and effort. The competition is structured in three separate tracks. You can participate independently in each of them. Participation in all three tracks is not mandatory.

In addition to the public award ceremony during the conference, we offer the winners of each track of the competition a reimbursement for part of the expenses incurred for attending the conference. More details here.