Chang Yue (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Zhixiu Guo (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Jun Dai, Xiaoyan Sun (Department of Computer Science, Worcester Polytechnic Institute), Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China)

The widespread use of mobile apps meets user needs but also raises security concerns. Current security analysis methods often fall short in addressing user concerns as they do not parse app behavior from the user's standpoint, leading to users not fully understanding the risks within the apps and unknowingly exposing themselves to privacy breaches. On one hand, their analysis and results are usually presented at the code level, which may not be comprehensible to users. On the other hand, they neglect to account for the users' perceptions of the app behavior. In this paper, we aim to extract user-related behaviors from apps and explain them to users in a comprehensible natural language form, enabling users to perceive the gap between their expectations and the app's actual behavior, and assess the risks within the inconsistencies independently. Through experiments, our tool emph{InconPreter} is shown to effectively extract inconsistent behaviors from apps and provide accurate and reasonable explanations. InconPreter achieves an inconsistency identification precision of 94.89% on our labeled dataset, and a risk analysis accuracy of 94.56% on widely used Android malware datasets. When applied to real-world (wild) apps, InconPreter identifies 1,664 risky inconsistent behaviors from 413 apps out of 10,878 apps crawled from Google Play, including the leakage of location, SMS, and contact information, as well as unauthorized audio recording, etc., potentially affecting millions of users. Moreover, InconPreter can detect some behaviors that are not identified by previous tools, such as unauthorized location disclosure in various scenarios (e.g. taking photos, chatting, and enabling mobile hotspots, etc.). We conduct a thorough analysis of the discovered behaviors to deepen the understanding of inconsistent behaviors, thereby helping users better manage their privacy and providing insights for privacy design in further app development.

View More Papers

Careful About What App Promotion Ads Recommend! Detecting and...

Shang Ma (University of Notre Dame), Chaoran Chen (University of Notre Dame), Shao Yang (Case Western Reserve University), Shifu Hou (University of Notre Dame), Toby Jia-Jun Li (University of Notre Dame), Xusheng Xiao (Arizona State University), Tao Xie (Peking University), Yanfang Ye (University of Notre Dame)

Read More

Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A...

Ningfei Wang (University of California, Irvine), Shaoyuan Xie (University of California, Irvine), Takami Sato (University of California, Irvine), Yunpeng Luo (University of California, Irvine), Kaidi Xu (Drexel University), Qi Alfred Chen (University of California, Irvine)

Read More

Ghidra: Is Newer Always Better?

Jonathan Crussell (Sandia National Laboratories)

Read More

DiStefano: Decentralized Infrastructure for Sharing Trusted Encrypted Facts and...

Sofia Celi (Brave Software), Alex Davidson (NOVA LINCS & Universidade NOVA de Lisboa), Hamed Haddadi (Imperial College London & Brave Software), Gonçalo Pestana (Hashmatter), Joe Rowell (Information Security Group, Royal Holloway, University of London)

Read More