无线局域网使用1D-CNN进行威胁检测
Marek Natkaniec1, Marcin Bednarz1
1Institute of Telecommunications, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一种机器学习算法,用于检测无线局域网 (WLAN) 中的二层威胁. 深度神经网络方法通过识别恶意流量模式来提高安全性,改进无线入侵检测系统 (WIDS).
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 无线局域网 (WLAN) 提供了方便的网络访问,但面临着越来越多的安全威胁,如干扰和注入攻击.
- 现有的安全措施难以有效地检测动态WLAN环境中的复杂的Layer 2威胁.
研究的目的:
- 提出和评估一种新的机器学习算法,用于检测WLAN中的Layer 2威胁.
- 通过先进的网络流量分析,增强无线入侵检测系统 (WIDS) 的功能.
主要方法:
- 利用深度神经网络 (DNN) 模型来分析网络流量模式.
- 开发了一个强大的数据集,包括预处理和数据分割,用于训练和测试DNN.
- 实现并测试了针对各种Layer 2攻击向量的拟议算法.
主要成果:
- 机器学习算法在识别恶意活动模式方面表现出高准确度.
- 实验结果显示,与现有方法相比,性能优越,特别是在精度方面.
- 拟议的方法有效地检测WLAN中的一系列Layer 2威胁.
结论:
- 开发的机器学习算法对于检测WLAN中的Layer 2威胁是有效的.
- 该解决方案可以集成到无线入侵检测系统 (WIDS) 中,以显著增强网络安全.
- 这些发现有助于更安全,更可靠的无线网络基础设施.
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