Ege Cem Kirci


Last Name

Kirci

First Name

Ege Cem

ORCID

0000-0002-7014-1971

Organisational unit

09477 - Vanbever, Laurent / Vanbever, Laurent

Filters

2022 - 20254
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Publications 1 - 4 of 4
  • Mass surveillance of VoIP calls in the data plane
    Item type: Conference Paper
    Kirci, Ege Cem; Apostolaki, Maria; Meier, Roland; et al. (2022)
    SOSR '22: Proceedings of the Symposium on SDN Research
    Over the last decade, programmable data planes have enabled highly customizable and efficient packet processing in commercial off-the-shelf hardware. Although researchers have demonstrated various use cases of this technology, its potential misuse has gained much less traction. This work investigates a typical surveillance scenario, VoIP call identification and monitoring, through a tailored data-plane attack. We introduce DELTA, a network-level side-channel attack that can efficiently identify VoIP calls and their hosting services. DELTA achieves this by tracking the inherent network footprint of VoIP services in the data plane. Specifically, DELTA stores the user addresses recently connected to VoIP services and links potential call flows with these addresses. We implement DELTA on existing hardware and conduct high-throughput tests based on representative traffic. DELTA can simultaneously store around 100 000 VoIP connections per service and identify call streams in-path, at line-rate, inside terabits of Internet traffic per second, immediately revealing users' communication patterns.
  • What is the next hop to more granular routing models?
    Item type: Conference Paper
    Kirci, Ege Cem; Torsiello, Valerio; Vanbever, Laurent (2024)
    HotNets '24: Proceedings of the 23rd ACM Workshop on Hot Topics in Networks
    Despite its widespread use, the "Gao-Rexford" model has long been recognized for its limitations in accurately capturing Internet routing behavior. However, the root causes of these limitations remain poorly understood, due to the lack of ground truth data. We address this by systematically analyzing inference techniques against generated topologies. Our findings reveal that the greatest issue with existing models is their lack of granularity, rather than the lack of data used to infer them. To overcome this limitation, we extend model granularity by incorporating internal topologies (at the router level) along with broader interdomain and intradomain routing policies. Additionally, we introduce an efficient pathfinding algorithm capable of computing router-level paths on Internet-scale topologies. Our results demonstrate that our extended model significantly improves path accuracy, reaching 86% with only 5% of vantage points---up from the 57% achieved by existing techniques with full visibility. These findings highlight the potential of finer-grained models to enhance path prediction and set the stage for a promising research agenda.
  • “Is my Internet down?”: Sifting through User-Affecting Outages with Google Trends
    Item type: Conference Paper
    Kirci, Ege Cem; Vahlensieck, Martin; Vanbever, Laurent (2022)
    IMC '22: Proceedings of the 22nd ACM Internet Measurement Conference
    What are the worst outages for Internet users? How long do they last, and how wide are they? Such questions are hard to answer via traditional outage detection and analysis techniques, as they conventionally rely on network-level signals and do not necessarily represent users’ perceptions of connectivity. We present SIFT, a detection and analysis tool for capturing user-affecting Internet outages. SIFT leverages users’ aggregated web search activity to detect outages. Specifically, SIFT starts by building a timeline of users’ interests in outage-related search queries. It then analyzes this timeline looking for spikes of user interest. Finally, SIFT characterizes these spikes in duration, geographical extent, and simultaneously trending search terms which may help understand root causes, such as power outages or associated ISPs. We use SIFT to collect more than 49 000 Internet outages in the United States over the last two years. Among others, SIFT reveals that user-affecting outages: (i) do not happen uniformly: half of them originate from 10 states only; (ii) can affect users for a long time: 10% of them last at least 3 hours; and (iii) can have a broad impact: 11% of them simultaneously affect at least 10 distinct states. SIFT annotations also reveal a perhaps overlooked fact: outages are often caused by climate and/or power-related issues.
  • Five Blind Men and the Internet: Towards an Understanding of Internet Traffic
    Item type: Conference Paper
    Kirci, Ege Cem; Mishra, Ayush; Vanbever, Laurent (2025)
Publications 1 - 4 of 4