The popularity of Wi-Fi networks coupled with the intrinsic vulnerability of wireless interfaces has promoted the investigation and proposal of traffic analysis and anomaly detection algorithms targeted to that application. We propose a scalable and modular algorithm architecture to set up a lightweight classifier, able to detect malicious frames with high reliability, allowing a simple implementation and suitable for real-time operations. We compare two design alternatives, based on either an optimized neuro-fuzzy classifier or a $k$-Nearest Neighbor classifier wrapped into a genetic optimization procedure. Both designs exploit a dissimilarity measure able to handle both numerical and non-numerical features. Scalability and modularity are obtained by considering an array of binary classifiers tuned to identify one specific attack against any other type of traffic. We exploit the Aegean Wi-Fi Intrusion Detection (AWID) dataset to assess the accuracy of the proposed algorithm, finding up to twelve out of the fourteen attack classes of the dataset can be identified with high reliability based just on the inspection of a single frame, provided the right features are observed.

Wi-Fi网络的普及以及无线接口的固有脆弱性促进了针对该应用的流量分析和异常检测算法的研究和提议。我们提出了一种可扩展的模块化算法体系结构，以建立轻量级分类器，该分类器能够以高可靠性检测恶意帧，从而实现简单且适合于实时操作。我们根据优化的神经模糊分类器或$ķ$-最近的邻居分类器包含在遗传优化程序中。两种设计均采用能够处理数字和非数字特征的相异性度量。可伸缩性和模块化是通过考虑一组二进制分类器而获得的，这些二进制分类器经过微调以识别针对任何其他类型流量的一种特定攻击。我们利用爱琴海Wi-Fi入侵检测（AWID）数据集来评估所提出算法的准确性，仅基于单个帧的检查，就可以以高可靠性识别出该数据集的十四种攻击类别中的多达十二种，只要观察到正确的功能。