Contact
TUTORIAL: Applying Machine Learning to detect Android Malware: AndroPyTool and OmniDroid
By Alejandro Martin, Raul Lara-Cabreras and David Camacho
Contents of the tutorial:
The possibilities and advantages of applying Machine Learning to solve the most diverse problems are beyond question. It has been proved how this wide set of techniques can help to address varied issues related to computer vision, natural language processing, fraud detection, robotics or bioinformatics, among many others. In this tutorial we aim to present the possibilities of this field when dealing with a complex, current and critical problem: the detection of malware in Android devices. As we will show, Machine Learning techniques such as classification and clustering algorithms, deep learning or evolutionary computation are currently being employed to detect those malware samples whose behaviour exhibits malicious patterns. Furthermore, we will explain the different tools designed for performing Android malware analysis and reverse engineering processes. Finally, we will describe in first place our framework AndroPyTool, aimed at extracting a wide set of features from Android applications with the goal of deeply charactering their behaviour and in second place the OmniDroid dataset, a comprehensive dataset of features from Android benign and malicious applications.
Intended audience:
Open to all audiences interested in malware detection and machine learning.