Skip to main content

The Mining of IP Landmarks for Internet Webcams

  • Conference paper
  • First Online:
Wireless Sensor Networks (CWSN 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1321))

Included in the following conference series:

  • 433 Accesses

Abstract

IP geolocation, which is important for the security of devices, heavily relies on the number of high-quality landmarks. As one kind of widely used Internet of Things (IoT) devices, Internet webcams are exposed to the Internet intentionally or unintentionally. While there are few researches on the methodology to extract landmarks for Internet webcams by now. In this paper, we proposed a framework GeoWAT to automatically generate landmarks from the watermarks of the webcams, which are accurate enough to improve some IP geolocation services. GeoWAT uses Optical Character Recognition (OCR) techniques to get text locations from the watermarks of public webcams on the Internet websites. Then GeoWAT queries the locations through online maps to get latitudes/longitudes of webcams as landmarks. We conducted experiments to evaluate the performance and effectiveness of GeoWAT in real world. Our results show that GeoWAT could automatically extract the locations of webcams with high precision and recall. Also, GeoWAT have got more accurate landmarks than other IP location services, such as IPIP, GeoLites2 and ipstack, on the webcams dataset we collected from the whole world.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ulltveit-Moe, N., Oleshchuk, V.A., Køien, G.M.: Location-aware mobile intrusion detection with enhanced privacy in a 5G context. Wirel. Pers. Commun. 57(3), 317–338 (2011). https://doi.org/10.1007/s11277-010-0069-6

  2. Choi, S.M.: Location-based information service method and mobile terminal therefor (2014)

    Google Scholar 

  3. Kim, E.: An automatic recommendation scheme of TV program contents for (IP) TV personalization. IEEE Trans. Broadcast. 57(3), 674–684 (2011)

    Google Scholar 

  4. Wang, Y.: Towards street-level client-independent IP geolocation. In: Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation, pp. 365–379. USENIX Association, Boston (2011)

    Google Scholar 

  5. Padamanabban, V.N.: Determining the geographic location of internet hosts. ACM SIGMETRICS Perform. Eval. Rev. 29(1) (2001)

    Google Scholar 

  6. Gueye, B.: Constraint-based geolocation of internet hosts. IEEE/ACM Trans. Netw. 14(6), 1063–6692 (2006)

    Google Scholar 

  7. Katz-Bassett, E.: Towards IP geolocation using delay and topology measurements. In: Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement, pp. 71–84. Association for Computing Machinery, Rio de Janeriro (2006)

    Google Scholar 

  8. Liu, H.: Mining checkins from location-sharing services for client-independent IP geolocation. In: IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, pp. 619–627. IEEE, Toronto (2014)

    Google Scholar 

  9. Jinxia, W.: IP geolocation technology research based on network measurement. In: 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, pp. 892–897. IEEE, Harbin (2016)

    Google Scholar 

  10. Laskar, Z.: Camera relocalization by computing pairwise relative poses using convolutional neural network. In: 2017 IEEE International Conference on Computer Vision Workshop (ICCVW). IEEE, Venice (2017)

    Google Scholar 

  11. Lerman, G.: Estimation of camera locations in highly corrupted scenarios: all about that base, no shape trouble. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE/CVF, Salt Lake City (2018)

    Google Scholar 

  12. Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 56–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_4

    Chapter  Google Scholar 

  13. Karatzas, D.: ICDAR 2015 competition on robust reading. In: Proceedings of the 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1156–1160. IEEE Computer Society, USA (2015)

    Google Scholar 

  14. Shi, B.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2298–2304 (2017)

    Google Scholar 

  15. Mishra, A.: Scene text recognition using higher order language priors. In: BMVC - British Machine Vision Conference, BMVA, Surrey, United Kingdom (2012)

    Google Scholar 

  16. Archana, G.: Recent named entity recognition and classification techniques: a systematic review. Comput. Sci. Rev. 29, 21–43 (2018)

    Article  Google Scholar 

  17. Georgescu: Named-entity-recognition-based automated system for diagnosing cybersecurity situations in IoT networks. Sensors 19(15), 3380(2019)

    Google Scholar 

  18. Dojchinovski, M., Kliegr, T.: Datasets and GATE evaluation framework for benchmarking Wikipedia-based NER systems. In: The International Conference on NLP and DBPEDIA. CEUR-WS.org (2013)

    Google Scholar 

  19. Pejic, A.: An expert system for tourists using Google Maps API. In: 7th International Symposium on Intelligent Systems and Informatics, pp. 317–322. IEEE, Subotica (2009)

    Google Scholar 

  20. Zamir, A.R., Shah, M.: Accurate image localization based on Google Maps street view. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 255–268. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_19

    Chapter  Google Scholar 

  21. Ting, K.M.: Precision and recall. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8_652

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Weizhong Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ren, Y., Li, H., Zhu, H., Sun, L., Wang, W., Li, Y. (2020). The Mining of IP Landmarks for Internet Webcams. In: Hao, Z., Dang, X., Chen, H., Li, F. (eds) Wireless Sensor Networks. CWSN 2020. Communications in Computer and Information Science, vol 1321. Springer, Singapore. https://doi.org/10.1007/978-981-33-4214-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-33-4214-9_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4213-2

  • Online ISBN: 978-981-33-4214-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics