Enhancing android application security: A novel approach using DroidXGB for malware detection based on permission analysis

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Security and Privacy Pub Date : 2023-12-11 DOI:10.1002/spy2.361
Pawan Kumar, Sukhdip Singh
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Abstract

The prevalence of malicious Android applications targeting the platform has introduced significant challenges in the realm of security testing. Traditional solutions have proven insufficient in handling the growing number of malicious apps, resulting in persistent exposure of Android smartphones to evolving forms of malware. This study investigates the potential of extreme gradient boosting (XGB) in identifying complex and high‐dimensional malicious permissions. By leveraging attribute combination and selection techniques, XGBoost demonstrates promising capabilities in this area. However, enhancing the XGBoost model presents a formidable challenge. To overcome this, This research employs adaptive grey wolf optimization (AGWO) for hyper‐parameter tuning. AGWO utilizes continuous values to represent the position and movement of the grey wolf, enabling XGBoost to search for optimal hyper‐parameter values in a continuous space. The proposed approach, DroidXGB, utilizes XGBoost and AGWO to analyze permissions and identify malware Android applications. It aims to address security vulnerabilities and compares its performance with baseline algorithms and state‐of‐the‐art methods using four benchmark datasets. The results showcase DroidXGB's impressive accuracy of 98.39%, outperforming other existing methods and significantly enhancing Android malware detection and security testing capabilities.
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增强安卓应用程序的安全性:基于权限分析使用 DroidXGB 检测恶意软件的新方法
针对安卓平台的恶意安卓应用程序的盛行给安全测试领域带来了巨大挑战。事实证明,传统的解决方案不足以应对日益增多的恶意应用程序,导致安卓智能手机持续暴露在不断演变的恶意软件面前。本研究探讨了极端梯度提升(XGB)在识别复杂和高维恶意权限方面的潜力。通过利用属性组合和选择技术,XGBoost 在这一领域展现出了良好的能力。然而,增强 XGBoost 模型是一项艰巨的挑战。为了克服这一难题,本研究采用了自适应灰狼优化(AGWO)技术来调整超参数。AGWO 利用连续值来表示灰狼的位置和移动,使 XGBoost 能够在连续空间中搜索最佳超参数值。所提出的方法 DroidXGB 利用 XGBoost 和 AGWO 分析权限并识别恶意 Android 应用程序。该方法旨在解决安全漏洞,并利用四个基准数据集将其性能与基准算法和最先进的方法进行了比较。结果表明,DroidXGB 的准确率高达 98.39%,优于其他现有方法,显著提高了安卓恶意软件检测和安全测试能力。
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