Decrypting Video Quality from Encrypted Streaming Traffic

International audienc

Textilzirkel in der DDR

Textilzirkel waren laienkünstlerische Gruppierungen, die in der DDR als Teil des so genannten "künstlerischen Volksschaffens" staatlich gefördert wurden. In ihrer Freizeit gestalteten und fertigten die Gruppen Kleidung, Souvenirs und Heimtextilien sowie Wandbehänge und Textilbilder für den Eigenbedarf oder für öffentliche Einrichtungen und gesellschaftliche Anlässe. Im Fokus dieser kulturwissenschaftlichen Arbeit stehen die KünstlerInnen, ihre Arbeitsweisen und die in den Gruppen entstandenen Werke. Durch eine Kombination aus Interviews, historischen Bild- und Textquellen sowie den materiellen Objekten wird ein umfassendes Gesamtbild der Textilzirkel gezeichnet. Dabei wird der kulturpolitische Hintergrund beleuchtet und die strukturellen sowie künstlerischen Entwicklungen im Laufe der Jahrzehnte reflektiert. Sie widerspiegeln zugleich den kulturellen und gesellschaftlichen Wandel innerhalb der DDR. In einem Ausblick wird der Verbleib der textilkünstlerischen Gruppierungen nach der Wiedervereinigung betrachtet

Targeted Attacks: Redefining Spear Phishing and Business Email Compromise

In today's digital world, cybercrime is responsible for significant damage to organizations, including financial losses, operational disruptions, or intellectual property theft. Cyberattacks often start with an email, the major means of corporate communication. Some rare, severely damaging email threats - known as spear phishing or Business Email Compromise - have emerged. However, the literature disagrees on their definition, impeding security vendors and researchers from mitigating targeted attacks. Therefore, we introduce targeted attacks. We describe targeted-attack-detection techniques as well as social-engineering methods used by fraudsters. Additionally, we present text-based attacks - with textual content as malicious payload - and compare non-targeted and targeted variants

BIGMOMAL — Big Data Analytics for Mobile Malware Detection

International audienceMobile malware is on the rise. Indeed, due to their popularity, smartphones represent an attractive target for cybercriminals, especially because of private user data, as these devices incorporate a lot of sensitive information about users, even more than a personal computer. As a matter of fact, besides personal information such as documents, accounts, passwords, and contacts, smartphone sensors centralise other sensitive data including user location and physical activities. In this paper, we study the problem of malware detection in smartphones, relying on supervised-machine-learning models and big-data analytics frameworks. Using the SherLock dataset, a large, publicly available dataset for smartphone-data analysis, we train and benchmark tree-based models to identify running applications and to detect malware activity. We verify their accuracy, and initial results suggest that decision trees are capable of identifying running apps and malware activity with high accuracy

Distributed Internet Paths Performance Analysis through Machine Learning

International audienceInternet path changes are frequently linked to path inflation and performance degradation; therefore, predicting their occurrence is highly relevant for performance monitoring and dynamic traffic engineering. In this paper we showcase DisNETPerf and NETPerfTrace, two different and complementary tools for distributed Internet paths performance analysis, using machine learning models

ViCrypt: Real-time, Fine-grained Prediction of Video Quality from Encrypted Streaming Traffic

International audienceWith the advent of HTTP Adaptive Streaming (HAS) technology, the visual quality of videos streamed over the Internet has become a paramount Key Performance Indicator (KPI) for Internet Service Providers (ISPs), who want to deliver a high video streaming Quality of Experience (QoE) to satisfy their customers and avoid churn. We address the problem of real-time QoE monitoring of HAS, from the ISP perspective, focusing on video-resolution, video bitrate and re-buffering prediction. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to predict these metrics in a fine-grained scale, using as input only packet-level data. The proposed measurement system performs predictions in real time, during the course of an ongoing video-streaming session, with a time granularity as small as one second. We consider the particular case of YouTube video streaming. Empirical evaluations on a large and heterogeneous corpus of YouTube measurements demonstrate that the proposed system can predict six different video resolution levels with very high accuracy -- from 144p to 1080p, estimate the video encoding bitrate as a regression problem with small estimation errors, and predict the occurrence of re-buffering events with high precision and recall, all of this in real time. Different from state of the art, the prediction task is not bound to coarse-grained video quality classes and does not require chunk-detection approaches for feature extraction. As an additional novelty, our methodology continuously extracts features from the encrypted stream of packets in a stream-like, recursive manner, using bounded - and lightweight - memory footprints; this enables its execution on top of limited memory hardware, such as set-top boxes or home routers, which are nowadays the most preferred devices for conducting end-customer monitoring by major vendors

Mobile web and app QoE monitoring for ISPs - from encrypted traffic to speed index through machine learning

International audienceWeb browsing is one of the key applications of the Internet. In this paper, we address the problem of mobile Web and App QoE monitoring from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Our study targets the analysis of Web and App QoE in mobile devices, including mobile browsing in smartphones and tablets, as well as mobile apps. As a proxy to Web QoE, we focus on the analysis of the well-known Speed Index (SI) metric. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI of individual web page and app loading sessions, using as input only packet level data. Empirical evaluations on a large, multi mobile-device corpus of Web and App QoE measurements for top popular websites and selected apps demonstrate that the proposed solution can properly infer the SI from in-network, encrypted-traffic measurements, relying on learning-based models. Our study also reveals relevant network and web page content characteristics impacting Web QoE in mobile devices, providing a complete overview on the mobile Web and App QoE assessment problem

Considering User Behavior in the Quality of Experience Cycle: Towards Proactive QoE-aware Traffic Management

International audienceThe concept of Quality of Experience (QoE) of Internet services is widely recognized by service providers and network operators. They strive to deliver the best experience to their customers in order to increase revenues and avoid churn. Therefore, QoE is increasingly considered as an integral part of the reactive traffic management cycle of network operators. Additionally, QoE also constitutes a cycle of its own, which includes the user behavior and the service requirements. This work describes this QoE cycle, which is not widely taken into account yet, discusses the interactions of the two cycles, and derives implications towards an improved and proactive QoE-aware traffic management. A showcase on how network operators can obtain hints on the change of network requirements from detecting user behavior in encrypted video traffic is also presented in this paper