通过对API呼叫分析应用的Swarm智能和自动编码技术来增强Android恶意软件检测
K S Ranadheer Kumar1, Jagadish Gurrala2
1Department of CSE, Koneru Lakshmaiah Education Foundation; 2202031117@kluniversity.in.
Journal of visualized experiments : JoVE
|January 19, 2026
概括
本研究确定了用于有效检测Android恶意软件的关键应用程序编程接口 (API) 调用功能. 使用群集智能,它以最小的功能实现了98.87%的准确性,增强了安全智能.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 软件工程 软件工程 软件工程
背景情况:
- 恶意软件安全情报依赖于分析应用程序数据的威胁.
- 应用程序编程接口 (API) 调用对于恶意软件检测至关重要.
- 减少功能空间可以提高恶意软件分析效率.
研究的目的:
- 确定重要的API调用功能,以精确检测Android恶意软件.
- 提高恶意软件识别系统的效率和准确性.
主要方法:
- 利用了群体智能优化技术:火优化,子搜索和殖民地优化.
- 使用自动编码器用于显著的特征提取.
- 使用机器学习分类器 (KNN,RF,SVM,DT,LR) 和混合神经分类器评估的特征集.
主要成果:
- 在恶意软件分类中达到98.87%的高精度.
- 在100个API调用功能中,仅使用了7个,证明了有效性.
- 一个混合人工神经分类器提高了恶意软件分类性能.
结论:
- 群体智能有效地识别了关键的API调用功能,用于Android恶意软件检测.
- 拟议的方法显著提高了检测精度和效率.
- 这种方法为改善移动安全情报提供了强大的解决方案.
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