Inferring Origin Flow Patterns in Wi-Fi with Deep Learning

We present a novel application of deep learning in networking. The envisioned system can learn the original flow characteristics such as a burst size and inter-burst gaps conceived at the source from packet sampling done at a receiver Wi-Fi node. This problem is challenging because CSMA introduces complex, irregular alterations to the origin pattern of the flow in the presence of competing flows. Our approach is semi-supervised learning. We first work through multiple layers of feature extraction and subsampling from unlabeled flow measurements. We use a feature extractor based on sparse coding and dictionary learning, and our subsampler performs overlapping max pooling. Given the layers of learned feature mapping, we train SVM classifiers with deep feature representation resulted at the top layer. The proposed scheme has been evaluated empirically in a custom wireless simulator and OPNET. The results are promising that we achieve superior classification performance over ARMAX, Naı̈ve Bayes classifiers, and Gaussian mixture models optimized by the EM algorithm.