research-article

Under the Concealing Surface: Detecting and Understanding Live Webcams in the Wild

Authors:

Limin SunAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 4, Issue 1

Article No.: 05, Pages 1 - 25

https://doi.org/10.1145/3379471

Published: 27 May 2020 Publication History

Abstract

Given the central role of webcams in monitoring physical surroundings, it behooves the research community to understand the characteristics of webcams' distribution and their privacy/security implications. In this paper, we conduct the first systematic study on live webcams from both aggregation sites and individual webcams (webpages/IP hosts). We propose a series of efficient, automated techniques for detecting and fingerprinting live webcams. In particular, we leverage distributed algorithms to detect aggregation sites and generate webcam fingerprints by utilizing the Graphical User Interface (GUI) of the built-in web server of a device. Overall, we observe 0.85 million webpages from aggregation sites hosting live webcams and 2.2 million live webcams in the public IPv4 space. Our study reveals that aggregation sites have a typical long-tail distribution in hosting live streams (5.8% of sites contain 90.44% of live streaming contents), and 85.4% of aggregation websites scrape webcams from others. Further, we observe that (1) 277,239 webcams from aggregation sites and IP hosts (11.7%) directly expose live streams to the public, (2) aggregation sites expose 187,897 geolocation names and more detailed 23,083 longitude/latitude pairs of webcams, (3) the default usernames and passwords of 38,942 webcams are visible on aggregation sites in plaintext, and (4) 1,237 webcams are detected as having been compromised to conduct malicious behaviors.

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Index Terms

Under the Concealing Surface: Detecting and Understanding Live Webcams in the Wild
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
2. Networks
  1. Network performance evaluation
    1. Network measurement
  2. Network types
    1. Cyber-physical networks

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 4, Issue 1

SIGMETRICS

March 2020

467 pages

EISSN:2476-1249

DOI:10.1145/3402934

Editors:
Augustin Chaintreau
Columbia University
,
Leana Golubchik
University of Southern California
,
Zhi-Li Zhang
University of Minnesota

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Publication History

Published: 27 May 2020

Online AM: 07 May 2020

Published in POMACS Volume 4, Issue 1

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National Natural Science Foundation of China
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https://doi.org/10.1109/TNET.2024.3422089
El Jaouhari SAhmed S(2024)CTIoT: A Cyber Threat Intelligence Tool for IoT2024 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC61514.2024.10592406(1483-1489)Online publication date: 27-May-2024
https://doi.org/10.1109/IWCMC61514.2024.10592406
Song JWan SHuang MLiu JSun LLi Q(2023)Toward Automatically Connecting IoT Devices with Vulnerabilities in the WildACM Transactions on Sensor Networks10.1145/360895120:1(1-26)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3608951
Wan SLi QWang HLi HSun L(2023)DevTag: A Benchmark for Fingerprinting IoT DevicesIEEE Internet of Things Journal10.1109/JIOT.2022.322558010:7(6388-6399)Online publication date: 1-Apr-2023
https://doi.org/10.1109/JIOT.2022.3225580
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