Advertisement

A Novel Method to Address Security Using Smart Gateway and Dynamic Authentication for Home Networks

  • Shruthi Sreedharan
  • N. Rakesh
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Networks within smart home paradigm are advancing at a pace today that spans many areas. There is a leap in density, protocols, types and methods of access of things into a smart home network today. Increasingly varying types of things demand for a monitoring at the administrator level that provides simplistic visibility and power for intelligent decisions. These decisions would be used to analyze and ascertain the security of the network and track unusual behavior.

Behavioral tracking of user profiles is explored in this work. The profiles themselves are a footprint of the smart device trying to gain access to the network. The following work intends to explore a methodology in incorporation of context based processing of the incoming packets to the network gateway. On their successful processing, the alerts raised for these anomalies are further analyzed for classification effectiveness and rated against established and relevant classifiers in Weka explorer tool.

Keywords

Authentication Intrusion detection IoT Smart home Snort 

Notes

Acknowledgements

This research is supported by Amrita School of Engineering, Bangalore.

References

  1. 1.
    Staudemeyer, R.C., Omlin, C.W.: Extracting salient features for network intrusion detection using machine learning methods. S. Afr. Comput. J. 52, 82–96 (2014)Google Scholar
  2. 2.
    Santoso, F., Yun, N.: Securing IoT for smart home system. In: IEEE International Symposium on Consumer Electronics (2015)Google Scholar
  3. 3.
    Lee, W., Stolfo, S., Mok, K.: Mining in data-flow environment: experience in network intrusion detection. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 114–124 (1999)Google Scholar
  4. 4.
    Habib, K., Leister, W.: Context-aware authentication for the internet of things. In: The Eleventh International Conference on Autonomic and Autonomous Systems (2015)Google Scholar
  5. 5.
    Kim, Y., Yoo, S., Yoo, C.: DAoT: dynamic and energy-aware authentication for smart home appliances in Internet of Things. In: IEEE International Conference on Consumer Electronics (2015)Google Scholar
  6. 6.
    Hodo, E., Bellekens, X., et al.: Threat analysis of IoT networks using artificial neural network intrusion detection system. In: International Symposium on Networks, Computers and Communications (2016)Google Scholar
  7. 7.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 028 (2000)Google Scholar
  8. 8.
    Srivastava, J.R, Sudarshan, T.S.B.: Intelligent traffic management with wireless sensor networks. In: ACS International Conference on Computer Systems and Applications (2013)Google Scholar
  9. 9.
    Srividya, Ch., Rakesh, N.: Enhancement and performance analysis of epidemic routing protocol for delay tolerant networks. In: International Conference on Inventive Systems and Control, pp. 1–5 (2017)Google Scholar
  10. 10.
    Ashwini, M., Rakesh, N.: Enhancement and performance analysis of LEACH algorithm in IOT. In: International Conference on Inventive Systems and Control (2017)Google Scholar
  11. 11.
    Lakshmi, R.V., Krishnan, D., Parvathy, S., Vishnudatha, K., Poroor, J., Dhar, A.: JPermit: usable and secure registration of guest-phones into enterprise VoIP network. In: International Conference on Advances in Computer Engineering (2010)Google Scholar
  12. 12.
    Manmadhan, N., Achuthan, K.: Behavioural analysis for prevention of intranet information leakage. In: International Conference on Advances in Computing, Communications and Informatics (2014)Google Scholar
  13. 13.
    Sforzin, A., Conti, M., Marmol, F.G., Bohli, J.: RPiDS: raspberry Pi IDS a fruitful intrusion detection system for IoT. In: International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (2016)Google Scholar
  14. 14.
    Garcia-Teodoro, P., Diaz-Verdejo, J., et al.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 028, 18–28 (2009)CrossRefGoogle Scholar
  15. 15.
    Gupta, R., Singh, S.: Intrusion detection system using SNORT. Int. Res. J. Eng. Technol. (IRJET) 04 (2017)Google Scholar
  16. 16.
    Zomlot, A., Sundaramurthy, S.C., et. al: Aiding intrusion analysis using machine learning. In: 12th International Conference on Machine Learning and Applications (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmrita School of Engineering, Bengaluru, Amrita Vishwa VidyapeethamBengaluruIndia

Personalised recommendations