分析和比较恶意软件检测的有效性:对机器学习方法的研究
Muhammad Azeem1, Danish Khan1, Saman Iftikhar2
1Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt Pakistan.
Heliyon
|January 8, 2024
概括
这项研究通过探索用于恶意软件检测的机器学习 (ML) 来增强网络安全. 随机森林模型实现了97.68%的准确性,提供了对在线威胁的强有力的保护.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 增加了对数字基础设施的依赖.
- 恶意软件的检测和分类是网络安全的关键挑战.
- 现有的恶意软件分析深度学习模型存在局限性.
研究的目的:
- 探索现代机器学习 (ML) 方法用于恶意软件检测和分类.
- 在UNSWNB15数据集上评估各种ML模型的有效性.
- 提高恶意软件识别技术的准确性和效率.
主要方法:
- 使用的机器学习算法:K-最近邻居 (KNN),额外树 (ET),随机森林 (RF),后勤回归 (LR),决策树 (DT) 和神经网络多层感知器 (nnMLP).
- 应用特征编码来将数据集转换为数值.
- 使用术语频率-反向文档频率 (TFIDF) 基于特征选择的.
- 在将其输入到ML模型进行分类之前,平衡了数据集.
主要成果:
- 随机森林模型实现了最高准确率的97.68%.
- 使用TFIDF增强模型性能进行特征选择.
- 所有测试的ML模型都在恶意软件分类中表现出不同程度的有效性.
结论:
- 机器学习为恶意软件检测提供了深度学习的有希望的替代方案.
- 随机森林是一种高度有效的恶意软件分类模型.
- 拟议的方法为开发先进的网络安全解决方案提供了坚实的基础.
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