Differentiating Between Mobile and Desktop Application Network Traffic

Tyler Percy, Jason Pittman, Department of Computer Science, High Point University, 1 North University Parkway, 27268

The last several years has shown explosive growth for both the mobile application marketplace as well as accompanying cybersecurity issues related to such applications. While there is some overlap with traditional personal computer or desktop cybersecurity concepts, mobile applications can present unique network security threats. For example, the ubiquitous nature of mobile computing coupled with always-on, always-available network services create a materially different operating environment for mobile applications. As a result, cybersecurity threats have shifted to mobile platforms with mobile specific malware and phishing attacks. This is compounded by the fact that mobile applications communicate using the same networking mechanisms as their desktop personal computer counterparts. Wi-Fi is one such example. At the same time, the literature is unclear as to whether the same application running on a mobile device and a personal computer on the same network is distinguishable. While there has been considerable work on identifying mobile applications using network traffic, there has not been investigation into potential differences between mobile and desktop. This creates a limitation in how mobile application specific network security controls can be implemented. Thus, this study sought to measure the potential differences in network traffic characteristics in a popular financial trading application with mobile and desktop versions. A simple quasi-experimental method was used to generate and collect network data. A set of analytic measures were then applied to the network data with a lens towards identifying differentiating characteristics. The results reveal no discernible difference between mobile and desktop versions of the same application.

Additional Abstract Information

Presenter: Tyler Percy

Institution: High Point University

Type: Poster

Subject: Computer Science

Status: Approved

Time and Location

Session: Poster 5
Date/Time: Tue 12:30pm-1:30pm
Session Number: 4012