Skip to main content
SpringerLink
Log in

User Relationship Classification of Facebook Messenger Mobile Data using WEKA

  • Conference paper
  • First Online:
Network and System Security (NSS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11058))

Included in the following conference series:

Abstract

Mobile devices are a wealth of information about its user and their digital and physical activities (e.g. online browsing and physical location). Therefore, in any crime investigation artifacts obtained from a mobile device can be extremely crucial. However, the variety of mobile platforms, applications (apps) and the significant size of data compound existing challenges in forensic investigations. In this paper, we explore the potential of machine learning in mobile forensics, and specifically in the context of Facebook messenger artifact acquisition and analysis. Using Quick and Choo (2017)’s Digital Forensic Intelligence Analysis Cycle (DFIAC) as the guiding framework, we demonstrate how one can acquire Facebook messenger app artifacts from an Android device and an iOS device (the latter is, using existing forensic tools. Based on the acquired evidence, we create 199 data-instances to train WEKA classifiers (i.e. ZeroR, J48 and Random tree) with the aim of classifying the device owner’s contacts and determine their mutual relationship strength.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Anglano, C., Canonico, M., Guazzone, M.: Forensic analysis of telegram messenger on android smartphones. Digit. Invest. 23, 31–49 (2017)

    Article  Google Scholar 

  2. Azuaje, F.: Witten IH, Frank E: data mining: practical machine learning tools and techniques 2nd edition. BioMed. Eng. OnLine 5(1), 51 (2006). https://doi.org/10.1186/1475-925X-5-51

    Article  Google Scholar 

  3. Barmpatsalou, K., Cruz, T., Monteiro, E., Simoes, P.: Current and future trends in mobile device forensics - a survey. ACM Comput. Surv. 51, 46 (2018)

    Article  Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  5. Cahyani, N.D.W., Ab Rahman, N.H., Glisson, W.B., Choo, K.K.R.: The role of mobile forensics in terrorism investigations involving the use of cloud storage service and communication apps. Mob. Netw. Appl. 22(2), 240–254 (2017)

    Article  Google Scholar 

  6. Daryabar, F., Dehghantanha, A., Choo, K.K.R.: Cloud storage forensics: mega as a case study. Aust. J. Forensic Sci. 49(3), 344–357 (2017). https://doi.org/10.1080/00450618.2016.1153714

    Article  Google Scholar 

  7. Dezfouli, F.N., Dehghantanha, A., Eterovic-Soric, B., Choo, K.K.R.: Investigating social networking applications on smartphones detecting Facebook, Twitter, LinkedIn and Google+ artefacts on android and iOS platforms. Aust. J. Forensic Sci. 48(4), 469–488 (2016). https://doi.org/10.1080/00450618.2015.1066854

    Article  Google Scholar 

  8. Lee, K., Palsetia, D., Narayanan, R., Patwary, M.M.A., Agrawal, A., Choudhary, A.: Twitter trending topic classification. In: IEEE 11th International Conference on Data Mining Workshops, pp. 251–258, December 2011. https://doi.org/10.1109/ICDMW.2011.171

  9. Marturana, F., Me, G., Berte, R., Tacconi, S.: A quantitative approach to triaging in mobile forensics. In: IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications, pp. 582–588, November 2011. https://doi.org/10.1109/TrustCom.2011.75

  10. Patil, T.R., Sherekar, S.: Performance analysis of Naive Bayes and J48 classification algorithm for data classification. Int. J. Comput. Sci. Appl. 6(2), 256–261 (2013)

    Google Scholar 

  11. Quick, D., Choo, K.K.R.: Pervasive social networking forensics: intelligence and evidence from mobile device extracts. J. Netw. Comput. Appl. 86, 24–33 (2017)

    Article  Google Scholar 

  12. Refaeilzadeh, P., Tang, L., Liu, H.: Cross-validation. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 532–538. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-39940-9_565

    Chapter  Google Scholar 

  13. Umair, A., Nanda, P., He, X.: Online social network information forensics: a survey on use of various tools and determining how cautious Facebook users are? In: IEEE Trustcom/BigDataSE/ICESS, pp. 1139–1144, August 2017. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.364

  14. Walnycky, D., Baggili, I., Marrington, A., Moore, J., Breitinger, F.: Network and device forensic analysis of android social-messaging applications. Digit. Invest. 14, S77–S84 (2015). https://doi.org/10.1016/j.diin.2015.05.009. http://www.sciencedirect.com/science/article/pii/S1742287615000547. The Proceedings of the Fifteenth Annual DFRWS Conference

    Article  Google Scholar 

  15. Yang, T.Y., Dehghantanha, A., Choo, K.K.R., Muda, Z.: Windows instant messaging app forensics: Facebook and skype as case studies. PLoS ONE 11(3), 1–29 (2016). https://doi.org/10.1371/journal.pone.0150300

    Article  Google Scholar 

Download references

Acknowledgments

The first author is supported by the Australian Government Research Training Program Scholarship.

Author information

Authors and Affiliations

  1. School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia

    Amber Umair, Priyadarsi Nanda & Xiangjian He

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, 78249-0631, USA

    Kim-Kwang Raymond Choo

Authors
  1. Amber Umair
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Priyadarsi Nanda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangjian He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kim-Kwang Raymond Choo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amber Umair .

Editor information

Editors and Affiliations

  1. The Hong Kong Polytechnic University, Hong Kong, China

    Man Ho Au

  2. The University of Hong Kong, Hong Kong, China

    Siu Ming Yiu

  3. Guangzhou University, Guangzhou, China

    Jin Li

  4. The Hong Kong Polytechnic University, Hong Kong, China

    Xiapu Luo

  5. City University of Hong Kong, Hong Kong, China

    Cong Wang

  6. University of Salerno and University of Naples Federico II, Fisciano, Italy

    Aniello Castiglione

  7. CISPA Helmholtz Center i.G. GmbH, Saarbrücken, Germany

    Kamil Kluczniak

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Umair, A., Nanda, P., He, X., Choo, KK.R. (2018). User Relationship Classification of Facebook Messenger Mobile Data using WEKA. In: Au, M., et al. Network and System Security. NSS 2018. Lecture Notes in Computer Science(), vol 11058. Springer, Cham. https://doi.org/10.1007/978-3-030-02744-5_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02744-5_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02743-8

  • Online ISBN: 978-3-030-02744-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation