返回
How Does Visualisation Help App Practitioners Analyse Android Apps?
DOI:10.1109/TDSC.2022.3178181.png)
摘要
En 中文
Behaviour analysis is essential for the security verification of suspicious Android applications, but analysts are usually faced with a huge obstacle when conducting the app behaviour analysis. They are expected to have comprehensive knowledge of different IT fields and a strong awareness of cyber threats. However, training a new security analyst typically requires a significant amount of time and can be extremely costly. Although there are tools available to assist analysts in studying Android behaviour and security, the completion of this task still heavily relies on the experience of the analysts. To address this problem, we recognise visualisation as a promising method and conduct a series of controlled experiments to demonstrate its effectiveness in the context of Android app behaviour and security analysis. We accordingly develop a visualisation tool based on apps' call graphs (CG) (named VisualDroid) and conduct an experiment and a follow-up interview. Compared to existing solutions, the results suggest that the CG-based visualisation solution (VisualDroid) can lower the barriers to Android behaviour and security analysis. The user study reveals that the platform includes CG-based visualisation components leads to a statistically significant improvement in Android behaviour analysis and security awareness. More specifically, it improves APK Analyzer, JD-GUI, JD-GUI+FlowDroid by 71.4%, 35.7%, and 39.2% in terms of the effectiveness of behaviour analysis. Participants who use VisualDroid also show improvements in the aspect of security awareness with an increase of 155% against APK Analyzer, 96% against JD-GUI, and 59.3% JD-GUI+FlowDroid.
Keyword:
Codes
Visualization
Security
Manuals
Inspection
Companies
Training
Android
visualisation
application comprehension
human-computer interaction
期刊
IF:
7.5
论文数:
2.6K
被引数:
9.6K
机构
引用论文
Understanding Android App Piggybacking: A Systematic Study of Malicious Code Grafting理解Android应用捎带: 恶意代码嫁接的系统研究
Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis基于频繁子图分析的Android恶意软件家族分类及代表性样本选择
Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android AppsAmandroid: 一种用于Android应用安全审查的精确通用组件间数据流分析框架

