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Android Source Code Vulnerability Detection: A Systematic Literature Review
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What this work says
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Comparative analysis of Android and iOS from security viewpoint
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Static analysis of android apps: A systematic literature review
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Dynamic Permissions based Android Malware Detection using Machine Learning Techniques
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A Comparison of Features for Android Malware Detection
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Machine Learning Methods for Software Vulnerability Detection
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Automatically Granted Permissions in Android apps
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Security Notifications in Static Analysis Tools: Developers’ Attitudes, Comprehension, and Ability to Act on Them
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Automatic Malicious Code Classification System through Static Analysis Using Machine Learning
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MagpieBridge: A General Approach to Integrating Static Analyses into IDEs and Editors (Tool Insights Paper)
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Applicability of Crowd Sourcing to Determine the Best Transportation Method by Analysing User Mobility
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AndroVul: a repository for Android security vulnerabilities.
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Software abstractions: logic, language and analysis
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Context-Aware Lightweight Framework for Source Code Vulnerability Detection
inferred← citeshttps://doi.org/10.3390/s24175465openalex-snapshot
Security Evaluation of Companion Android Applications in IoT: The Case of Smart Security Devices
inferred← citeshttps://doi.org/10.3390/s25061816openalex-snapshot
An Improved Software Source Code Vulnerability Detection Method: Combination of Multi-Feature Screening and Integrated Sampling Model
inferred← citeshttps://doi.org/10.3390/technologies11030076openalex-snapshot
Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses
inferred← citeshttps://doi.org/10.37394/23209.2024.21.5openalex-snapshot
The Role of Mobile Applications in a Foreign Language Learning
inferred← citeshttps://doi.org/10.4018/979-8-2600-0747-1.ch006openalex-snapshot
Design of Comprehensive Mobile Device Vulnerability Checking Application
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Enhancing Mobile Security through Comprehensive Penetration Testing
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Labelled Vulnerability Dataset on Android Source Code (LVDAndro) to Develop AI-Based Code Vulnerability Detection Models
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