在物联网中通过K-近邻算法检测Android恶意软件.
Himanshi Babbar1, Shalli Rani1, Dipak Kumar Sah2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究介绍了一种机器学习框架,用于预测物联网设备上的Android恶意软件. K-Nearest Neighbor模型实现了93%的预测率,提高了消费者设备的安全性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 在物联网生态系统中,Android设备越来越普遍.
- 越来越多的Android恶意软件对系统安全和用户隐私构成重大威胁.
- 预测和减轻这些威胁对于安全的物联网运行至关重要.
研究的目的:
- 开发和评估基于机器学习的框架,用于预测物联网设备中的Android恶意软件.
- 通过识别和阻止恶意应用程序来提高消费者设备的安全性.
- 通过高效的恶意软件检测,最大限度地减少能源的使用.
主要方法:
- 一个基于互联网的框架,利用静态分析进行特征提取.
- 多个机器学习算法的比较,包括天真贝叶斯,决策树,支持矢量机器和K-最近邻居 (KNN).
- 由于其性能,K-Nearest Neighbor (KNN) 被选为被提议的模型.
主要成果:
- K-Nearest Neighbor (KNN) 模型在Android恶意软件中实现了93%的最高预测率.
- KNN表现出强的表现,准确率为93%,精度为95%,回忆率为90%,F1得分为92%.
- 该系统在超过10,000个测试的Android应用中有效地识别了恶意应用程序.
结论:
- 拟议的机器学习框架,特别是KNN模型,可以有效地预测物联网设备上的Android恶意软件.
- 该系统成功地帮助阻止恶意应用程序,从而保护云数据和用户隐私.
- 这种方法提供了一种节能解决方案,用于保护消费者物联网设备免受不断变化的Android威胁.
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