activity
20172019
most citedLarge-Scale Analysis of Framework-Specific Exceptions in Android Apps

115 citations · 200 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SE2019★ 7 cited

StoryDroid: Automated Generation of Storyboard for Android Apps

Sen Chen, Lingling Fan, Chunyang Chen +4

Mobile apps are now ubiquitous. Before developing a new app, the development team usually endeavors painstaking efforts to review many existing apps with similar purposes. The revi…

cs.CR2019★ 5 cited

A Large-Scale Empirical Study on Industrial Fake Apps

Chongbin Tang, Sen Chen, Lingling Fan +4

While there have been various studies towards Android apps and their development, there is limited discussion of the broader class of apps that fall in the fake area. Fake apps and…

cs.SE2018★ 51 cited

Efficiently Manifesting Asynchronous Programming Errors in Android Apps

Lingling Fan, Ting Su, Sen Chen +4

Android, the #1 mobile app framework, enforces the single-GUI-thread model, in which a single UI thread manages GUI rendering and event dispatching. Due to this model, it is vital…

cs.CR2018★ 13 cited

An Empirical Assessment of Security Risks of Global Android Banking Apps

Sen Chen, Lingling Fan, Guozhu Meng +5

Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial los…

cs.SE2018★ 115 cited

Large-Scale Analysis of Framework-Specific Exceptions in Android Apps

Lingling Fan, Ting Su, Sen Chen +5

Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to f…

cs.CR2017★ 9 cited

Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach

Sen Chen, Minhui Xue, Lingling Fan +4

The evolution of mobile malware poses a serious threat to smartphone security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via pol…