128
Views
3
CrossRef citations to date
0
Altmetric
Abstract

Security issues in wireless sensor network – A survey

, &

References

  • Dionisis Kandris, Christos Nakas, Dimitrios Vomvas and Grigorios Koulouras, (2020). Applications of Wireless Sensor Networks: An Up-to-Date Survey. Appl. Syst. Innov., 3, 14; doi:https://doi.org/10.3390/asi3010014. (2020).
  • MarketandMarket research done on Wireless Sensor Networks which forecastregrionandglobalto 2023 https://www.marketsandmarkets.com/Market-Reports/wireless-sensor-networks-market-445.html (2019).
  • Md Abdul Azeem, Dr. Khaleel-ur-Rahman khan, A.V. Pramod. Security Architecture Framework and Secure Routing Protocols in Wireless Sensor Networks – Survey. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.4. (2011)
  • D. Praveen Kumar, Tarachand Amgoth, Chandra Sekhara Rao Annavarapu. Machine learning algorithms for wireless sensor networks: A survey, Fusion, doi: https://doi.org/https://doi.org/10.1016/j.inffus. (2018).
  • Okan CAN, Ozgur Koray SAHINGOZ. A Survey of Intrusion Detection Systems in Wireless Sensor Networks, 6th International Conference on Modeling, Simulation, and Applied Optimization(ICMSAO). doi:https://doi.org/10.1109/icmsao.2015.7152200 (2015).
  • S. Zhu et al., “An Interleaved Hop-by-Hop Authentication Scheme for Filtering of Injected False Data in Sensor Networks,” Proc. IEEE Symp. Security and Privacy, Oakland, CA, pp. 259–71, (2004).
  • F. Ye et al., “Statistical En-Route Filtering of Injected False Datasensor Networks,” Proc. IEEE INFOCOM, Hong Kong, (2004).
  • J. Deng, R. Han, and S. Mishra, “INSENS: Intrusion-Tolerant Routing Wireless Sensor Networks,” Department of Computer Science, University of Colorado, Tech. Report CU CS-939-02, (2002).
  • G. Wang et al., “On supporting Distributed Collaboration Sensor Networks,” Proc. MILCOM, (2003).
  • D. Praveen Kumar, Tarachand Amgoth, Chandra Sekhara Rao Annavarapu “Machine learning algorithms for wireless sensor networks: A survey”, Elsevier Information Fusion, Volume 49, Pages 1-25, (2019).
  • Gupta, A., Pandey, O., Shukla, M., Dadhich, A., Mathur, S., Ingle, A., 2013. Computational intelligence based intrusion detection systems for wireless communication and pervasive computing networks, In: Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on, pp. 1–7(2013).
  • Lee, T.-H., Wen, C.-H., Chang, L.-H., Chiang, H.-S., Hsieh, M.-C., 2014. A lightweight intrusion detection scheme based on energy consumption analysis in 6LowPAN. In: Huang, Y.-M., Chao, H.-C., Deng, D.-J., Park, J.J.J.H. (Eds.), Advanced Technologies, Embedded and Multimedia for Human-centric Computing, Lecture Notes in Electrical Engineering 260. Springer, Netherlands, 1205–1213, (2014).
  • Summerville, D.H., Zach, K.M., Chen, Y., 2015. Ultra-lightweight deep packet anomaly detection for Internet of Things devices. In: 2015 IEEE Proceedings of the 34th International Performance Computing and Communications Conference (IPCCC), IEEE, pp. 1–8, (2015).
  • Thanigaivelan, N.K., Nigussie, E., Kanth, R.K., Virtanen, S., Isoaho, J., 2016. Distributed internal anomaly detection system for Internet-of-Things. In: 2016 Proceedings of the 13th IEEE Annual Consumer Communications Networking Conference (CCNC), pp. 319–320, (2016).
  • M. Wazid, A.K. Das, An efficient hybrid anomaly detection scheme using K-means clustering for wireless sensor networks, Wireless Personal Commun. 90 (4) 1971–2000, (2016). doi: https://doi.org/10.1007/s11277-016-3433-3
  • Z. Feng, J. Fu, D. Du, F. Li, S. Sun, A new approach of anomaly detection in wireless sensor networks using support vector data description, Int. J. Distrib. Sens. Netw. 13 (1) 1–14, (2017). doi: https://doi.org/10.1177/1550147716686161
  • H.S. Emadi, S.M. Mazinani, A novel anomaly detection algorithm using DBSCAN and SVM in wireless sensor networks, Wireless Personal Commun. 98 (2) 2025–2035, (2018). doi: https://doi.org/10.1007/s11277-017-4961-1
  • P. Gil, H. Martins, F. Januário, Outliers detection methods in wireless sensor networks, Artif. Intell. Rev. 1–26, (2018).
  • Satvik Vats & B. B. Sagar (2019) Performance evaluation of K-means clustering on Hadoop infrastructure, Journal of Discrete Mathematical Sciences and Cryptography, 22:8, 1349-1363, DOI: https://doi.org/10.1080/09720529.2019.1692444

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.