Contextual counters and multimodal Deep Learning for activity-level traffic classification of mobile communication apps during COVID-19 pandemic.

The COVID-19 pandemic has reshaped Internet traffic due to the huge modifications imposed to lifestyle of people resorting more and more to collaboration and communication apps to accomplish daily tasks. Accordingly, these dramatic changes call for novel traffic management solutions to adequately countermeasure such unexpected and massive changes in traffic characteristics. In this paper, we focus on communication and collaboration apps whose traffic experienced a sudden growth during the last two years. Specifically, we consider nine apps whose traffic we collect, reliably label, and publicly release as a new dataset (MIRAGE-COVID-CCMA-2022) to the scientific community. First, we investigate the capability of state-of-art single-modal and multimodal Deep Learning-based classifiers in telling the specific app, the activity performed by the user, or both. While we highlight that state-of-art solutions reports a more-than-satisfactory performance in addressing app classification (96%-98% F-measure), evident shortcomings stem out when tackling activity classification (56%-65% F-measure) when using approaches that leverage the transport-layer payload and/or per-packet information attainable from the initial part of the biflows. In line with these limitations, we design a novel set of inputs (namely Context Inputs) providing clues about the nature of a biflow by observing the biflows coexisting simultaneously. Based on these considerations, we propose Mimetic-All a novel early traffic classification multimodal solution that leverages Context Inputs as an additional modality, achieving ≥ 82 % F-measure in activity classification. Also, capitalizing the multimodal nature of Mimetic-All, we evaluate different combinations of the inputs. Interestingly, experimental results witness that Mimetic-ConSeq-a variant that uses the Context Inputs but does not rely on payload information (thus gaining greater robustness to more opaque encryption sub-layers possibly going to be adopted in the future)-experiences only ≈ 1 % F-measure drop in performance w.r.t. Mimetic-All and results in a shorter training time.

Learning Wireless Sensor Networks for Source Localization.

Source localization and target tracking are among the most challenging problems in wireless sensor networks (WSN). Most of the state-of-the-art solutions are complicated and do not meet the processing and memory limitations of the existing low-cost sensor nodes. In this paper, we propose computationally-cheap solutions based on the support vector machine (SVM) and twin SVM (TWSVM) learning algorithms in which network nodes firstly detect the desired signal. Then, the network is trained to specify the nodes in the vicinity of the source (or target); hence, the region of event is detected. Finally, the centroid of the event region is considered as an estimation of the source location. The efficiency of the proposed methods is shown by simulations.