Feature generalization using AWID dataset.

The widespread use of wireless networks to transfer an enormous amount of sensitive information has caused a plethora of vulnerabilities and privacy issues. The management frames, particularly authentication and association frames, are vulnerable to cyberattacks and it is a significant concern. Existing research in Wi-Fi attack detection focused on obtaining high detection accuracy while neglecting modern traffic and attack scenarios such as key reinstallation or unauthorized decryption attacks. This study proposed a novel approach using the AWID 3 dataset for cyberattack detection. The retained features were analyzed to assess their transferability, creating a lightweight and cost-effective model. A decision tree with a recursive feature elimination method was implemented for the extraction of the reduced features subset, and an additional feature wlan_radio.signal_dbm was used in combination with the extracted feature subset. Several deep learning and machine learning models were implemented, where DT and CNN achieved promising classification results. Further, feature transferability and generalizability were evaluated, and their detection performance was analyzed across different network versions where CNN outperformed other classification models. The practical implications of this research are crucial for the secure automation of wireless intrusion detection frameworks and tools in personal and enterprise paradigms.无线网络被广泛用于传输海量敏感信息，由此催生了大量漏洞与隐私安全隐患。其中，管理帧（尤其是认证与关联帧）极易遭受网络攻击，该问题已成为重要的安全关切点。现有Wi-Fi攻击检测领域的研究多专注于提升检测准确率，却忽视了现代流量与攻击场景，例如密钥重装攻击、未授权解密攻击等。本研究提出一种基于AWID 3数据集（AWID 3 Dataset）的新型网络攻击检测方案。研究对筛选保留的特征开展可迁移性分析，以构建轻量且高性价比的模型：采用结合递归特征消除法的决策树提取精简特征子集，并额外引入特征wlan_radio.signal_dbm与该特征子集进行融合。本研究还实现了多种深度学习与机器学习模型，其中决策树（Decision Tree，DT）与卷积神经网络（Convolutional Neural Network，CNN）取得了优异的分类效果。此外，研究对特征的可迁移性与泛化能力进行了评估，并在不同网络版本场景下分析了检测性能，结果显示卷积神经网络的检测性能优于其他分类模型。本研究的实际应用价值，对于个人与企业场景下的无线入侵检测框架及工具的安全自动化具有关键意义。