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@Inproceedings { 2020_delacadena_trafficsliver,
   title = {TrafficSliver: Fighting Website Fingerprinting Attacks with Traffic Splitting},
   year = {2020},
   month = {11},
   day = {12},
   pages = {1971-1985},
   abstract = {Website fingerprinting (WFP) aims to infer information about the content of encrypted and anonymized connections by observing patterns of data flows based on the size and direction of packets. By collecting traffic traces at a malicious Tor entry node — one of the weakest adversaries in the attacker model of Tor — a passive eavesdropper can leverage the captured meta-data to reveal the websites visited by a Tor user. As recently shown, WFP is significantly more effective and realistic than assumed. Concurrently, former WFP defenses are either infeasible for deployment in real-world settings or defend against specific WFP attacks only.
To limit the exposure of Tor users to WFP, we propose novel lightweight WFP defenses, TrafficSliver, which successfully counter today’s WFP classifiers with reasonable bandwidth and latency overheads and, thus, make them attractive candidates for adoption in Tor. Through user-controlled splitting of traffic over multiple Tor entry nodes, TrafficSliver limits the data a single entry node can observe and distorts repeatable traffic patterns exploited by WFP attacks. We first propose a network-layer defense, in which we apply the concept of multipathing entirely within the Tor network. We show that our network-layer defense reduces the accuracy from more than 98\% to less than 16\% for all state-of-the-art WFP attacks without adding any artificial delays or dummy traffic. We further suggest an elegant client-side application-layer defense, which is independent of the underlying anonymization network. By sending single HTTP requests for different web objects over distinct Tor entry nodes, our application-layer defense reduces the detection rate of WFP classifiers by almost 50 percentage points. Although it offers lower protection than our network-layer defense, it provides a security boost at the cost of a very low implementation overhead and is fully compatible with today’s Tor network.},
   keywords = {Traffic Analysis; Website Fingerprinting; Privacy; Anonymous Communication; Onion Routing; Web Privacy},
   url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2020/2020-delacadena-trafficsliver.pdf},
   web_url = {https://github.com/TrafficSliver},
   publisher = {ACM},
   booktitle = {Proceedings of the 27th ACM SIGSAC Conference on Computer and Communications Security (CCS '20), November 9-13, 2020, Orlando, FL, USA},
   event_place = {Virtual Event, USA},
   event_date = {November 9-13, 2020},
   ISBN = {978-1-4503-7089-9/20/11},
   DOI = {10.1145/3372297.3423351},
   reviewed = {1},
   author = {De la Cadena, Wladimir and Mitseva, Asya and Hiller, Jens and Pennekamp, Jan and Reuter, Sebastian and Filter, Julian and Wehrle, Klaus and Engel, Thomas and Panchenko, Andriy}
}

@Article { 2020_gleim_factDAG,
   title = {FactDAG: Formalizing Data Interoperability in an Internet of Production},
   journal = {IEEE Internet of Things Journal},
   year = {2020},
   month = {4},
   day = {14},
   volume = {7},
   number = {4},
   pages = {3243-3253},
   abstract = {In the production industry, the volume, variety and velocity of data as well as the number of deployed protocols increase exponentially due to the influences of IoT advances. While hundreds of isolated solutions exist to utilize this data, e.g., optimizing processes or monitoring machine conditions, the lack of a unified data handling and exchange mechanism hinders the implementation of approaches to improve the quality of decisions and processes in such an interconnected environment.
The vision of an Internet of Production promises the establishment of a Worldwide Lab, where data from every process in the network can be utilized, even interorganizational and across domains. While numerous existing approaches consider interoperability from an interface and communication system perspective, fundamental questions of data and information interoperability remain insufficiently addressed.
In this paper, we identify ten key issues, derived from three distinctive real-world use cases, that hinder large-scale data interoperability for industrial processes. Based on these issues we derive a set of five key requirements for future (IoT) data layers, building upon the FAIR data principles. We propose to address them by creating FactDAG, a conceptual data layer model for maintaining a provenance-based, directed acyclic graph of facts, inspired by successful distributed version-control and collaboration systems. Eventually, such a standardization should greatly shape the future of interoperability in an interconnected production industry.},
   keywords = {Data Management; Data Versioning; Interoperability; Industrial Internet of Things; Worldwide Lab},
   tags = {internet-of-production},
   url = {https://comsys.rwth-aachen.de/fileadmin/papers/2020/2020-gleim-iotj-iop-interoperability.pdf},
   publisher = {IEEE},
   ISSN = {2327-4662},
   DOI = {10.1109/JIOT.2020.2966402},
   reviewed = {1},
   author = {Gleim, Lars and Pennekamp, Jan and Liebenberg, Martin and Buchsbaum, Melanie and Niemietz, Philipp and Knape, Simon and Epple, Alexander and Storms, Simon and Trauth, Daniel and Bergs, Thomas and Brecher, Christian and Decker, Stefan and Lakemeyer, Gerhard and Wehrle, Klaus}
}