miscellaneous-6

Mobile Security

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in mobile-security-6

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find mobile-security-6 models and implementations

Datasets

Dataset of Context information for Zero Interaction Security

Subtasks

No subtasks available.

Most implemented papers

AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis Approach

Hanan Hindy, A. Amin, Amgad Eldessouki, M. Magdy, Nouran Abdeen, Islam Hegazy•Mon Oct 21 2019

This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications, and detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy.

41

Content

0

Paper Graph

Robustness of on-Device Models: Adversarial Attack to Deep Learning Models on Android Apps

Chunyang Chen, Yujin Huang, Han Hu•Mon Jan 11 2021

This study proposes a simple but effective approach to hacking deep learning models using adversarial attacks by identifying highly similar pre-trained models from TensorFlow Hub, and investigates the characteristics ofDeep learning models used by hundreds of Android apps on Google Play.

42 0

Paper Graph

Deep Learning for Android Malware Defenses: A Systematic Literature Review

C. Tantithamthavorn, Yepang Liu, Yue Liu, Li Li•Mon Mar 08 2021

A systematic literature review was conducted to search and analyze how DL approaches have been applied in the context of malware defenses in the Android environment and reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios.

105 0

Paper Graph

Heterogeneous Temporal Graph Transformer: An Intelligent System for Evolving Android Malware Detection

Yanfang Ye, Shifu Hou, Yujie Fan, Mingxuan Ju, Wenqiang Wan, Kui Wang, Y. Mei, Qi Xiong•Fri Aug 13 2021

This paper proposes and develops an intelligent system named Dr.Droid to jointly model malware propagation and evolution for their detection at the first attempt, and proposes a novel heterogeneous temporal graph transformer framework (denoted as HTGT) to integrate both spatial and temporal dependencies while preserving the heterogeneity.

56 0

Paper Graph

Adding a benchmark result helps the community track progress.