12th International Symposium on Intelligent Distributed Computing
IDC 2018
15-17 October 2018, Bilbao, Spain

Sponsored by:

Latest news:

June 4th, 2018:
Updated notification deadline: June 5th, 2018

May 1st, 2018:
Submission deadline extended: May 15th, 2018 (no more extensions will be granted)

April 9, 2018:
Submission deadline extended: May 1st, 2018.

March 26, 2018:
New tutorial: ANDROPYTOOL.

March 6, 2018:
Confirmed Special Issue on Applied Soft Computing.

March 6, 2018:
New invited speaker: Albert Bifet.

January 19, 2018:
New Accepted Workshop: ML-PdM.

December 13, 2017:
New tutorial: JMETALSP.

December 13, 2017:
New tutorial: KMBD.

December 7, 2017:
New invited speakers: Francisco Herrera and Eleni I. Vlahogianni.

December 5, 2017:
New Accepted Workshop: COMPSUS.

November 15, 2017:
New Accepted Workshop: INDILOG.

November 6, 2017:
Confirmed Special Issue on Future Generation Computer Systems.

October 30, 2017:
Definitive conference dates published.

October 25, 2017:
Tentative conference dates published.

October 20, 2017:
First CFP published.

October 19, 2017:
Invited speakers: Jose A. Lozano and David Camacho.

October 18, 2017:
IDC 2018 web site was launched.

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.

Tutorial format:

Mainly practical.