将NLP和集体学习集成到下一代防火墙中,用于在边缘计算中强大的恶意软件检测
Ramahlapane Lerato Moila1, Mthulisi Velempini1
1Department of Computer Science, University of Limpopo, Polokwane 0727, South Africa.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种自然语言处理 (NLP) 框架,用于下一代防火墙 (NGFWs),以对抗边缘计算中的恶意软件. 该系统在检测网络威胁方面实现了高精度,增强了边缘环境的安全性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 边缘计算环境容易受到复杂的恶意软件攻击,这些攻击挑战了传统的安全措施.
- 越来越依赖边缘基础设施,需要先进的威胁检测和缓解策略.
- 非结构化的威胁情报为识别新型攻击载体提供了丰富的数据来源.
研究的目的:
- 提出一个新的框架,整合自然语言处理 (NLP) 和集体学习,用于边缘计算中的恶意软件检测.
- 增强下一代防火墙 (NGFW) 的实时威胁识别和缓解能力.
- 开发一个可扩展和可适应的安全解决方案,优化对边缘环境的约束.
主要方法:
- 利用像TF-IDF矢量化这样的NLP技术来处理非结构化的威胁情报 (例如报告,日志).
- 实施一个集体学习模型,将随机森林 (RF) 和后勤回归 (LR) 结合起来,使用软投票.
- 使用ANOVA和混矩阵分析验证模型的稳定性.
主要成果:
- 通过合成数据增强,在网络威胁情报数据集上实现了95%的准确性.
- 在CSE-CIC-IDS2018数据集上实现了98%的准确性.
- 通过验证分析证明了低错误率和确认的统计稳定性.
结论:
- 拟议的NLP和集体学习框架有效地检测和减轻边缘计算环境中的恶意软件.
- 该系统提供了增强的检测速率和适应性,对于资源有限的边缘部署至关重要.
- 这种方法为在网络边缘运行的下一代防火墙提供了一个可扩展的防御层.
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