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Reseach Article

Review of Machine Learning Techniques for Detection of Routing Attacks in Wireless Sensor Network

by Shraddha Sarode
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 27
Year of Publication: 2021
Authors: Shraddha Sarode
10.5120/ijca2021921657

Shraddha Sarode . Review of Machine Learning Techniques for Detection of Routing Attacks in Wireless Sensor Network. International Journal of Computer Applications. 183, 27 ( Sep 2021), 30-34. DOI=10.5120/ijca2021921657

@article{ 10.5120/ijca2021921657,
author = { Shraddha Sarode },
title = { Review of Machine Learning Techniques for Detection of Routing Attacks in Wireless Sensor Network },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2021 },
volume = { 183 },
number = { 27 },
month = { Sep },
year = { 2021 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number27/32099-2021921657/ },
doi = { 10.5120/ijca2021921657 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:18:03.561027+05:30
%A Shraddha Sarode
%T Review of Machine Learning Techniques for Detection of Routing Attacks in Wireless Sensor Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 27
%P 30-34
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wireless sensor networks are widely applied in many fields like transportation, urban terrain tracking, healthcare, precision agriculture, etc. However, this deployment has introduced new security concerns. These security concerns involve two kinds of attacks on wireless sensor networks active and passive. Passive attacks are launched to observe the network without disrupting network functionality. Active attacks can disrupt the function of the network and can be initiated on layers of communication protocol. Active attacks that are related to network layer routing attacks are presented. For the last decade, machine learning algorithms have been used in many important applications, including detection of routing attacks. The objective of this paper is to review machine learning algorithms that can be used to detect routing attacks in wireless sensor networks. In this paper, evaluation parameters and challenges of applying machine learning algorithms in wireless sensor networks are also discussed. These challenges can serve as potential future research directions.

References
  1. Kumar, D. P., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1-25.
  2. Improving the Effectiveness of Diabetic Retinopathy Models, https://ai.googleblog.com/2018/12/improving-effectiveness-of-diabetic.html
  3. Das, K., Majumdar, S., Moulik, S., & Fujita, M. (2020, September). Real-Time Threshold-based Landslide Prediction System for Hilly Region using Wireless Sensor Networks. In 2020 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan) (pp. 1-2). IEEE.
  4. Riaz, M. N., Buriro, A., & Mahboob, A. (2018). Classification of attacks on wireless sensor networks: A survey. International Journal of Wireless and Microwave Technologies, 8(6), 15-39.
  5. Virmani, D., Soni, A., Chandel, S., & Hemrajani, M. (2014). Routing attacks in wireless sensor networks: A survey. arXiv preprint arXiv:1407.3987.
  6. Humaira, F., Islam, M. S., Nur, F. N., & Hussain, K. A. (2020, July). A Comprehensive Study on Machine Learning Algorithms for Wireless Sensor Network Security. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
  7. Quincozes, S. E., & Kazienko, J. F. (2020, June). Machine Learning Methods Assessment for Denial of Service Detection in Wireless Sensor Networks. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT) (pp. 1-6). IEEE.
  8. Xiao, Z., Liu, C., & Chen, C. (2009, December). An anomaly detection scheme based on machine learning for WSN. In 2009 First International Conference on Information Science and Engineering (pp. 3959-3962). IEEE.
  9. Kaplantzis, S., Shilton, A., Mani, N., & Sekercioglu, Y. A. (2007, December). Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information (pp. 335-340). IEEE.
  10. Mounica, M., Vijayasaraswathi, R., & Vasavi, R. (2021). Detecting Sybil Attack In Wireless Sensor Networks Using Machine Learning Algorithms. In IOP Conference Series: Materials Science and Engineering (Vol. 1042, No. 1, p. 012029). IOP Publishing.
  11. Khan, R. A., & Pathan, A. S. K. (2018). The state-of-the-art wireless body area sensor networks: A survey. International Journal of Distributed Sensor Networks, 14(4), 1550147718768994.
  12. Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H. P. (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys & Tutorials, 16(4), 1996-2018.
  13. Gunduz, S., Arslan, B., & Demirci, M. (2015, December). A review of machine learning solutions to denial-of-services attacks in wireless sensor networks. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 150-155). IEEE.
  14. Al-issa, A. I., Al-Akhras, M., ALsahli, M. S., & Alawairdhi, M. (2019, April). Using machine learning to detect DoS attacks in wireless sensor networks. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 107-112). IEEE.
  15. Mamdouh, M., Elrukhsi, M. A., & Khattab, A. (2018, August). Securing the internet of things and wireless sensor networks via machine learning: A survey. In 2018 International Conference on Computer and Applications (ICCA) (pp. 215-218). IEEE.
  16. Patil, B., & Agarkhed, J. (2020, October). An Exploratory Machine Learning Technique for Investigating Intrusion in Wireless Sensor Networks. In 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC) (pp. 1-6). IEEE.
  17. Narayanan, K. L., Krishnan, R. S., Julie, E. G., Robinson, Y. H., & Shanmuganathan, V. (2021). Machine Learning Based Detection and a Novel EC-BRTT Algorithm Based Prevention of DoS Attacks in Wireless Sensor Networks. Wireless Personal Communications, 1-25.
  18. Kumar, N. S., Suryaprabha, E., & Hariprasath, K. (2021). Machine learning based hybrid model for energy efficient secured transmission in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 1-16.
  19. Kim, T., Vecchietti, L. F., Choi, K., Lee, S., & Har, D. (2020). Machine Learning for Advanced Wireless Sensor Networks: A Review. IEEE Sensors Journal.
  20. Ahmad, B., Jian, W., Ali, Z. A., Tanvir, S., & Khan, M. S. A. (2019). Hybrid anomaly detection by using clustering for wireless sensor network. Wireless Personal Communications, 106(4), 1841-1853.
  21. Yu, D., Kang, J., & Dong, J. (2021). Service Attack Improvement in Wireless Sensor Network Based on Machine Learning. Microprocessors and Microsystems, 80, 103637.
  22. Aledhari, M., Razzak, R., & Parizi, R. M. (2021). Machine learning for network application security: Empirical evaluation and optimization. Computers & Electrical Engineering, 91, 107052.
  23. Otoum, S., Kantarci, B., & Mouftah, H. T. (2019). On the feasibility of deep learning in sensor network intrusion detection. IEEE Networking Letters, 1(2), 68-71.
  24. Poornima, I. G. A., & Paramasivan, B. (2020). Anomaly detection in wireless sensor network using machine learning algorithm. Computer Communications, 151, 331-337.
  25. Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd Ed., Han, Kamber& Pei, University of Illinois at Urbana-Champaign &Simon Fraser University, 2011
  26. Amutha, J., Sharma, S., & Sharma, S. K. (2021). Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions. Computer Science Review, 40, 100376.
  27. Al-Akhras, M., Al-Issa, A. I., Alsahli, M. S., & Alawairdhi, M. (2020, November). POSTER: Feature Selection to Optimize DoS Detection in Wireless Sensor Networks. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) (pp. 263-265). IEEE.
  28. Wazid, M., & Das, A. K. (2016). An efficient hybrid anomaly detection scheme using K-means clustering for wireless sensor networks. Wireless Personal Communications, 90(4), 1971-2000.
  29. Raghav, R. S., Thirugnansambandam, K., & Anguraj, D. K. (2020). Beeware routing scheme for detecting network layer attacks in wireless sensor networks. Wireless Personal Communications, 112(4), 2439-2459.
  30. Almomani, I., Al-Kasasbeh, B., & Al-Akhras, M. (2016). WSN-DS: A dataset for intrusion detection systems in wireless sensor networks. Journal of Sensors, 2016.
  31. Intelligence and Security Informatics Data Sets, https://www.azsecure-data.org/other-data.html
  32. Labelled Wireless Sensor Network Data Repository (LWSNDR), https://www.uncg.edu/cmp/downloads/lwsndr.html
Index Terms

Computer Science
Information Sciences

Keywords

Machine learning wireless sensor network security routing attacks detection security.