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Interne Kommunikation ist ein wesentlicher Faktor in den einzelnen Ebenen einer Organisation. Je nach Unternehmenskultur wird eine mehr oder weniger offen geführte Kommunikation praktiziert und beinhaltet unterschiedliche Informationsformen, um firmenrelevante Inhalte an das gesamte Personal zu übermitteln. So kann dies etwa mit klassischen Rundmails erfolgen, oder durch Emanzipation der Belegschaft anhand von Kommunikationsplattformen. Einerseits gibt es dabei das klassische Intranet für Wissens- und Informationsmanagement, oder für mehr mobilere Erreichbarkeit des Personals etwa Mitarbeiter-Apps. Durch Mitarbeiter-Apps kann in den gesamten Unternehmensebenen interagiert werden, sie bewirken eine standortübergreifende Informationsübertragung und schaffen ein weitrechendes internes Vernetzen im Unternehmen. Dennoch steht die Einführung einer solchen App vor diversen Herausforderungen, welche in dieser Arbeit behandelt werden. Für eine erfolgreiche Implementierung einer Mitarbeiter-App, werden die gewonnenen Erkenntnisse anhand einer ausführlichen Literaturrecherche dargelegt und um einen Praxisteil, in Form von Experteninterviews, ergänzt. Dies soll für Unternehmen eine Hilfestellung sein, um die Einführung einer Mitarbeiter-App in Organisationen umzusetzen. Resultierend aus dieser Arbeit bewirken Mitarbeiter-Apps einen Wandel in der Unternehmenskultur und stellen Unternehmen jedoch vor die Frage, inwiefern diese Kommunikationsplattformen einen Mehrwehrt für jedes einzelne Unternehmen darstellt. Internal communication is an essential factor in the individual levels of an organization. Depending on the associated corporate culture, a more or less open communication is practiced and contains different forms of information transmission and how the entire staff receive company relevant content. This can be done with traditional emails or using communication platforms by emancipating employees. There is either the classic intranet for knowledge and information management or employee apps to have a higher mobile reachability of all employees in the company. This central platform promotes interaction on each level in the organization and causes transferred information across different sites and creates a higher internal networking in the whole enterprise. Nevertheless, the introduction of such an app faces various challenges, which are analyzed and covered in this master's thesis. For a successfully implementation of an employee app the research is based on a detailed literature search and supplemented by a practical part, gained by interviews with experts. The results of both aspects, support companies during the implantation process of an employee app. The result of this master's thesis is that employee apps bring a change in a corporate culture. Nevertheless, these apps are faced with the question what is the added value for each company by implementing these communication tools in an enterprise. Verfasserin: Susanne Zach Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers Masterarbeit FH JOANNEUM 2023

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.

The lockdowns and lifestyle changes during the COVID-19 pandemic have caused a measurable impact on Internet traffic in terms of volumes and application mix, with a sudden increase of usage of communication and collaboration apps. In this work, we focus on five such apps, whose traffic we collect, reliably label at fine granularity (per-activity), and analyze from the viewpoint of traffic classification. To this aim, we employ state-of-art deep learning approaches to assess to which degree the apps, their different use cases (activities), and the pairs app-activity can be told apart from each other. We investigate the early behavior of the biflows composing the traffic and the effect of tuning the dimension of the input, via a sensitivity analysis. The experimental analysis highlights the figures of the different architectures, in terms of both traffic-classification performance and complexity w.r.t. different classification tasks, and the related trade-off. The outcome of this analysis is informative for a number of network management tasks, including monitoring, planning, resource provisioning, and (security) policy enforcement.

In this work, we address the characterization and modeling of the network traffic generated by communication and collaboration apps which have been the object of recent traffic surge due to the COVID-19 pandemic spread. In detail, focusing on five of the top popular mobile apps (collected via the MIRAGE architecture) used for working/studying during the pandemic time frame, we provide characterization at trace and flow level, and modeling by means of Multimodal Markov Chains for both apps and related activities. The results highlight interesting peculiarities related to both the running applications and the specific activities performed. The outcome of this analysis constitutes the stepping stone toward a number of tasks related to network management and traffic analysis, such as identification/classification and prediction, and modern IT management in general.