Skip to main content

ARCSECURE: Centralized Hub for Securing a Network of IoT Devices

  • Conference paper
  • First Online:
Intelligent Computing

Abstract

As far as it is considered, IoT has been a game changer in the advancement of technology. In the current context, the major issue that users face is the threat to their information stored in these devices. Modern day attackers are aware of vulnerabilities in existence in the current IoT environment. Therefore, securing information from being gone into the hands of unauthorized parties is of top priority. With the need of securing the information came the need of protecting the devices which the data is being stored. Small Office/Home Office (SOHO) environments working with IoT devices are particularly in need of such mechanism to protect the data and information that they hold in order to sustain their operations. Hence, in order come up with a well-rounded security mechanism from every possible aspect, this research proposes a plug and play device “ARCSECURE”.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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

Similar content being viewed by others

References

  1. Zhang, Z., et al.: IoT Security: Ongoing Challenges and Research Opportunities (2014)

    Google Scholar 

  2. Ahmad, M., Salah, K.: IoT security : review, blockchain solutions, and open challenges. Futur. Gener. Comput. Syst. 82, 395–411 (2018)

    Article  Google Scholar 

  3. Lin, H., Bergmann, N.W.: IoT Privacy and Security Challenges for Smart Home Environments (2016)

    Google Scholar 

  4. Wurm, J., Hoang, K., Arias, O., Sadeghi, A.R., Jin, Y.: Security analysis on consumer and industrial IoT devices. In: Proceedings of Asia South Pacific Design Automation Conference ASP-DAC, vol. 25–28 January, pp. 519–524 (2016)

    Google Scholar 

  5. Jonsdottir, G., Wood, D., Doshi, R.: IoT network monitor. In: 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, pp. 1–5 (2017) Accessed 22 Feb 2020

    Google Scholar 

  6. Yu, E., Cho, S.: Ga-SVM wrapper approach for feature subset selection in keystroke dynamics identity verification. In: Neural Networks, 2003. Proceedings of the International Joint Conference on, vol. 3, pp. 2253–2257. IEEE (2003)

    Google Scholar 

  7. Revett, K., Gorunescu, F., Gorunescu, M., Ene, M., Magalhaes, S., Santos, H.: A machine learning approach to keystroke dynamics-based user authentication. Int. J. Electron. Secur. Digit. Forensics 1(1), 55–70 (2007)

    Article  Google Scholar 

  8. Zahid, S., Shahzad, M., Khayam, S.A., Farooq, M.: Keystroke-based user identification on smart phones. In: Kirda, E., Jha, S., Balzarotti, D. (eds.) Recent Advances in Intrusion Detection, pp. 224–243. Springer, Berlin (2009). https://doi.org/10.1007/978-3-642-04342-0_12

    Chapter  Google Scholar 

  9. Moose, F.S.: moosefs.com, MooseFS, 03 2019. https://moosefs.com/. Accessed 20 Feb 2020

  10. Chonka, A., Singh, J., Zhou, W.: Chaos theory based detection against network mimicking DDoS attacks. IEEE Commun. Lett. 13(9), 717–719 (2009)

    Article  Google Scholar 

  11. Mukkamala, S., Sung, A.H.: Detecting denial of service attacks using support vector machines. In: Institute for Complex Additive Systems Analysis New Mexico Tech Socorro, The IEEE International Conference on Fuzzy Systems (2003)

    Google Scholar 

  12. El-Hajj, M., Fadlallah, A., Chamoun, M., Serhrouchni, A.: A survey of internet of things (IoT) authentication schemes. Sensors (Switzerland) 19(5), 1–43 (2019). https://doi.org/10.3390/s19051141

    Article  Google Scholar 

  13. Muliono, Y., Ham, H., Darmawan, D.: Keystroke dynamic classification using machine learning for password authorization. Procedia Comput. Sci. 135, 564–569 (2018). https://doi.org/10.1016/j.procs.2018.08.209

    Article  Google Scholar 

  14. Geneiatakis, D., Kounelis, I., Neisse, R., Nai-Fovino, I., Steri, G., Baldini, G.: Security and privacy issues for an IoT based smart home (2017). https://doi.org/10.23919/MIPRO.2017.7973622

  15. Andrew, D., Michael, O.A.: A Study of the Advances in IoT Security, pp.1–5 (2018). https://doi.org/10.1145/3284557.3284560

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abeywardena, K.Y., Abeykoon, A.M.I.S., Atapattu, A.M.S.P.B., Jayawardhane, H.N., Samarasekara, C.N. (2021). ARCSECURE: Centralized Hub for Securing a Network of IoT Devices. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-030-80129-8_70

Download citation

Publish with us

Policies and ethics