1. Twitter Augmented Android Malware Detection
Jordan DeLoach – CIS 598 – February 13, 2017

2. Background
Android Malware can be extremely complicated and are designed to evade detection.
Even state of the art detection techniques like static analysis and dynamic analysis can be evaded.
In many instances, end users are providing millions of comments of relevant feedback via social media but this discussion is not currently utilized by any method to evaluate an app.

3. Project Description
Build a solution to be able to leverage Twitter data in addition to data from an app binary to train a machine learning classifier.
The goal is to show that I can more accurately detect malware by including social media data as elements in the future.

4. Architectural Overview
Machine Learning Pipeline
Machine Learning Pipeline
Machine Learning Pipeline
Machine Learning Pipeline

5. Tools

6. Requirements
Construct a system that can collect tweets and Android app binaries
Extract discriminative features from the apps and tweets
Establish a method to link tweets with Android apps
Train, test, and evaluate classifier performance
Both for linking approach and overall ML performance
We will train/test on two levels:
- Linking strategies
- Overall malware detection

7. Timeline February March April May Gather data Write code
Craft preliminary experiments
March
Continue coding
Focus on refined, publishable experiments
April
Compile results in presentable format
May
Finalize report/results

8. Questions