For the first time since the establishment of TCP and UDP, the Internet transport layer is subject to a major change by the introduction of QUIC. Initiated by Google in 2012, QUIC provides a reliable, connection-oriented low-latency and fully encrypted transport. In this paper, we provide the first broad assessment of QUIC usage in the wild. We monitor the entire IPv4 address space since August 2016 and about 46% of the DNS namespace to detected QUIC-capable infrastructures. Our scans show that the number of QUIC-capable IPs has more than tripled since then to over 617.59 K. We find around 161K domains hosted on QUIC-enabled infrastructure, but only 15K of them present valid certificates over QUIC. Second, we analyze one year of traffic traces provided by MAWI, one day of a major European tier-1 ISP and from a large IXP to understand the dominance of QUIC in the Internet traffic mix. We find QUIC to account for 2.6% to 9.1% of the current Internet traffic, depending on the vantage point. This share is dominated by Google pushing up to 42.1% of its traffic via QUIC.

Mining is the foundation of blockchain-based cryptocurrencies such as Bitcoin rewarding the miner for finding blocks for new transactions. The Monero currency enables mining with standard hardware in contrast to special hardware (ASICs) as often used in Bitcoin, paving the way for in-browser mining as a new revenue model for website operators. In this work, we study the prevalence of this new phenomenon. We identify and classify mining websites in 138M domains and present a new fingerprinting method which finds up to a factor of 5.7 more miners than publicly available block lists. Our work identifies and dissects Coinhive as the major browser-mining stakeholder. Further, we present a new method to associate mined blocks in the Monero blockchain to mining pools and uncover that Coinhive currently contributes 1.18% of mined blocks having turned over 1293 Moneros in June 2018. CCS CONCEPTS• Security and privacy → Malware and its mitigation; • Networks → Network measurement;

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Under intermittent Internet connectivity, enabling interaction between smart objects and mobile users in the Internet of Things (IoT) becomes a challenge. We thus discuss the notion of a "Challenged IoT" and propose Direct Interaction with Smart Challenged Objects (DISCO), enabling objects to define their interaction patterns and interface. Building on the distinct features of Bluetooth Low Energy (BLE), objects then convey their interface directly to mobile users. DISCO mitigates the need for Internet connectivity and pre-installed interfaces, i.e., smartphone apps, of existing approaches and proposes autonomous and local interaction with smart objects as a challenged network scenario. We implement DISCO for Android and iOS smartphones as well as Linux and Arduino objects and illustrate the design space of interaction patterns with Augmented Reality (AR) interaction based on visual object recognition within the tangible interaction sphere of the user. Our system evaluation shows the immediate real-life feasibility and applicability of DISCO on current hardware.

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Controlling physical machinery and processes is at the core of production automation. However, challenged by inflexibility, automation and control is evaluating to outsource this control to resourceful cloud environments. While this enables to derive better control through a plethora of measurements, it challenges the control quality through delay introduced through networks. In this paper, we show how to unify control and communication by offloading delay sensitive control tasks from the cloud to local network elements-a previously unexplored area for in-network processing-enabling both, ultra high quality-of-control while enabling scalable orchestration through cloud environments. Our implementation demonstrates how we combine state of the art control with communication. We achieve this by expressing the control and the datapath in P4 which we synthesize to BPF programs that we execute in XDP environments on Netronome SmartNICs. Further, we highlight the demands of control towards communication to build more involved and complex in-network controllers.

By offering the possibility to already perform processing as packets traverse the network, programmable data planes open up new perspectives for applications suffering from strict latency and high bandwidth requirements. Real-time Computer Vision (CV), with its high data rates and often mission-and safety-critical roles in the control of autonomous vehicles and industrial machinery, could particularly benefit from executing parts of its logic within network elements.In this paper, we thus explore what it takes to bring CV to the network. We present our work-in-progress efforts of implementing a line-following algorithm based on convolution filters on a P4-programmable NIC. We find that by appropriately identifying regions of interest in the image data and by diligently distributing the necessary calculations among the various match/action stages of the ingress-and egress pipelines of the NIC, our prototypical implementation can achieve over 19 decisions per second on 640x480 px grayscale images with filters large enough to guide a small autonomous car through various courses. CCS CONCEPTS• Networks → In-network processing; Middle boxes / network appliances; Programmable networks; • Computing methodologies → Computer vision;