Advertisement

Detecting False Messages in the Smartphone Fault Reporting System

  • Sharmiladevi Rajoo
  • Pritheega MagalingamEmail author
  • Ganthan Narayana Samy
  • Nurazean Maarop
  • Norbik Bashah Idris
  • Bharanidharan Shanmugam
  • Sundaresan Perumal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)

Abstract

The emergence of the Internet of Things (IoT) in Smart City allows mobile application developers to develop reporting services with an aim for local citizens to interact with municipalities regarding city issues in an efficient manner. However, the credibility of the messages sent rise as a great challenge when users intentionally send false reports through the application. In this research, an evidence detection framework is developed and divided into three parts that are a data source, IoT device’s false text classification engine and output. Text-oriented digital evidence from an IoT mobile reporting service is analyzed to identify suitable text classifier and to build this framework. The Agile model that consists of define, design, build and test is used for the development of the false text classification engine. Focus given on text-based data that does not include encrypted messages. Our proposed framework able to achieve 97% of accuracy and showed the highest detection rate using SVM compared to other classifiers. The result shows that the proposed framework is able to aid digital forensic evidence experts in their initial investigation on detecting false report of a mobile reporting service application in the IoT environment.

Keywords

Internet of Things Smartphone Application Reporting services Smart City Text classifiers 

Notes

Acknowledgement

This work is supported in part by Redtone IOT Sdn. Bhd., Malaysia. We thank Redtone IoT staff members who provided insight and data that greatly assisted the research.

References

  1. 1.
    Walravens, N.: Mobile city applications for Brussels citizens: Smart City trends, challenges and a reality check. Telematics Inform. 32(2), 282–299 (2015)CrossRefGoogle Scholar
  2. 2.
    Cook, J.: Tell the City of Seattle about potholes and graffiti with new ‘Find It, Fix It’ app (2013). https://www.geekwire.com/2013/city-seattle-potholes-graffiti-find-fix-it-app/. Accessed 15 July 2019
  3. 3.
    (Greece), T.I.T.I.-M.G. Improve My City. http://smartcityapps.urenio.org/improve-my-city_en.html. Accessed 15 July 2019
  4. 4.
    REDtoneIOTSdn.Bhd., CitiAct – Your Key in Building the Next Smart City Applications (2016)Google Scholar
  5. 5.
    Aggarwal, C.C., Abdelzaher, T.: Social sensing. In: Managing and Mining Sensor Data, pp. 237–297. Springer, Heidelberg (2013)Google Scholar
  6. 6.
    Kantarci, B., Mouftah, H.T.: Trustworthy sensing for public safety in cloud-centric internet of things. IEEE Internet Things J. 1(4), 360–368 (2014)CrossRefGoogle Scholar
  7. 7.
    Wang, D., Huang, C.: Confidence-aware truth estimation in social sensing applications. In: 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE (2015)Google Scholar
  8. 8.
    Marshall, J., Wang, D.: Towards emotional-aware truth discovery in social sensing applications. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE (2016)Google Scholar
  9. 9.
    Marshall, J., Syed, M., Wang, D.: Hardness-aware truth discovery in social sensing applications. In: 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE (2016)Google Scholar
  10. 10.
    Ghosh, N., et al.: A probabilistic approach for filtering out spam reports in a vehicular participatory sensing system. In: 2016 8th International Conference on Communication Systems and Networks (COMSNETS). IEEE (2016)Google Scholar
  11. 11.
    Barnwal, R.P., et al.: Enhancing reliability of vehicular participatory sensing network: a bayesian approach. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE (2016)Google Scholar
  12. 12.
    Prandi, C., et al.: A trustworthiness model for crowdsourced and crowdsensed data. In: 2015 IEEE Trustcom/BigDataSE/ISPA. IEEE (2015)Google Scholar
  13. 13.
    Pandey, U., Chakravarty, S.: A survey on text classification techniques for e-mail filtering. In: 2010 Second International Conference on Machine Learning and Computing (ICMLC). IEEE (2010)Google Scholar
  14. 14.
    Tayal, D.K., et al.: Crime detection and criminal identification in India using data mining techniques. AI Soc. 30(1), 117–127 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Firoozjaei, M.D., J. Park, and H. Kim. Detecting false emergency requests using callers’ reporting behaviors and locations. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE (2016)Google Scholar
  16. 16.
    Bhatti, F., et al.: A novel internet of things-enabled accident detection and reporting system for smart city environments. Sensors 19(9), 2071 (2019)CrossRefGoogle Scholar
  17. 17.
    Al-Zaidy, R., et al.: Mining criminal networks from unstructured text documents. Digit. Invest. 8(3–4), 147–160 (2012)CrossRefGoogle Scholar
  18. 18.
    Marturana, F., et al.: A quantitative approach to triaging in mobile forensics. In: 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE (2011)Google Scholar
  19. 19.
    Lakshmi, R.D., Radha, N.: Spam classification using supervised learning techniques. In: Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India. ACM (2010)Google Scholar
  20. 20.
    Trivedi, S.K., Dey, S.: Interaction between feature subset selection techniques and machine learning classifiers for detecting unsolicited emails. ACM SIGAPP Appl. Comput. Rev. 14(1), 53–61 (2014)CrossRefGoogle Scholar
  21. 21.
    Wang, S., et al.: Detecting android malware leveraging text semantics of network flows. IEEE Trans. Inf. Forensics Secur. 13(5), 1096–1109 (2018)CrossRefGoogle Scholar
  22. 22.
    Gautam, G., Yadav, D.: Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In: 2014 Seventh International Conference on Contemporary computing (IC3). IEEE (2014)Google Scholar
  23. 23.
    Munassar, N.M.A., Govardhan, A.: A comparison between five models of software engineering. IJCSI 5, 95–101 (2010)Google Scholar
  24. 24.
    Torgo, L.: Data mining with R. Learning by case studies. University of Porto, LIACC-FEP (2003). http://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR/. Accessed 1 July 2019
  25. 25.
    Panigrahi, P.K.: A comparative study of supervised machine learning techniques for spam e-mail filtering. In: 2012 Fourth International Conference on Computational Intelligence and Communication Networks. IEEE (2012)Google Scholar
  26. 26.
    Brindha, S., Prabha, K., Sukumaran, S.: A survey on classification techniques for text mining. In: 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE (2016)Google Scholar
  27. 27.
    Rajoo, S., et al.: A comparative study of text classifier for mobile crowdsensing applications. Adv. Sci. Lett. 24(1), 686–689 (2018)CrossRefGoogle Scholar
  28. 28.
    Gopal, S., et al.: Statistical learning for file-type identification. In: 2011 10th International Conference on Machine Learning and Applications and Workshops (ICMLA). IEEE (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sharmiladevi Rajoo
    • 1
  • Pritheega Magalingam
    • 1
    Email author
  • Ganthan Narayana Samy
    • 1
  • Nurazean Maarop
    • 1
  • Norbik Bashah Idris
    • 2
  • Bharanidharan Shanmugam
    • 3
  • Sundaresan Perumal
    • 4
  1. 1.Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.International Islamic Universiti MalaysiaGombakMalaysia
  3. 3.School of Engineering and Information TechnologyCharles Darwin UniversityCasuarinaAustralia
  4. 4.Faculty of Science and TechnologyUniversiti Sains Islam MalaysiaGombakMalaysia

Personalised recommendations