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Enhancing Android Security: A Static Analysis Framework for Vulnerability Detection

  • Unique Paper ID: 177439
  • Volume: 11
  • Issue: 12
  • PageNo: 727-731
  • Abstract:
  • The sudden proliferation of Android apps has resulted in a growing demand for effective security solutions to safeguard user data and privacy. Static analysis is an important method for detecting vulnerabilities in Android applications without running them, and thus it is an effective method for early-stage security analysis. In this paper, a new static framework for the vulnerability analysis of Android applications is described to identify usual security weaknesses such as insecure storage of data, misuse of permissions, and suspicious API calls. The proposed framework makes use of advanced program analyses, which encompass data flow analysis and control flow analysis, in order to dynamically analyze application code for suspected risks. Through the combination of automation and a large rule-based vulnerability detection mechanism, the proposed framework improves the precision and effectiveness of security analysis. Experimental results on real-world Android applications show that the framework is effective in detecting vulnerabilities with high accuracy. This work makes a contribution to the area of mobile security by giving developers and security analysts an effective tool to enhance the security position of Android applications Core Methodologies: A combination of rule-based detection processes, automated security tools, and program analysis techniques of advanced nature are the primary methods employed in this framework for static vulnerability analysis of Android applications. The initial step is to utilize AndroGaurd, which offers an automated framework for decompiling, scanning, and analyzing Android apps for security vulnerabilities. Precise taint analysis is achieved using FlowDroid, which follows the flow of sensitive information to detect malicious behavior and leaks. Code analysis and reverse engineering are two applications of AndroGuard, which generates data from the APK file and detects known vulnerabilities. Execution paths of the application and data flow are also determined through the application of CFG (Control Flow Graph) and DFG (Data Flow Graph) analysis. Also, machine learning-based anomaly detection is integrated into the system. Performance Insights: The performance of an Android vulnerability detection static analysis framework is mostly defined by its precision, efficiency, and scalability. The suggested framework maximizes performance through the use of a mix of lightweight and deep analysis methods. Tools such as AndroGaurd offer quick initial evaluations, rapidly identifying typical vulnerabilities, while FlowDroid's taint analysis provides a deeper analysis of data flows, providing a balance between efficiency and accuracy. For optimization of efficiency, parallel processing and caching are used to cut down redundant computation and accelerate large application analysis. Heuristic-based filtering is also utilized to reduce false positives by narrowing rule-based detection prior to performing deeper scans. The architecture also uses incremental analysis, in which previously scanned parts of an application are reused to prevent reprocessing of unchanged code, drastically improving scalability. Compared to current tools, this strategy is shown to provide greater detection accuracy with decreased computational cost and thus is an efficient solution for security analysts and developers dealing with large numbers of Android applications
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 727-731

Enhancing Android Security: A Static Analysis Framework for Vulnerability Detection

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