Abstract
Machine Learning is capable of providing real-time solutions that maximize the utilization of resources in the network thereby increasing the lifetime of the network. It is able to process automatically without being externally programmed thus making the process more easy, efficient, cost-effective, and reliable. ML algorithms can handle complex data more quickly and accurately. Machine Learning is used to enhance the ability of the Wireless Sensor Network environment. Wireless Sensor Networks (WSN) is a combination of several networks and it is decentralized and distributed in nature. WSN consists of sensor nodes and sinks nodes which have a property of self-organizing and self-healing. WSN is used in other applications, such as biodiversity and ecosystem protection, surveillance, climate change tracking, and other military applications.Now-a-days, a huge development is seen in WSNs due to the advancement of electronics and wireless communication technologies, several drawbacks like low computational capacity, small memory, and limited energy resources infrastructure needs physical vulnerability to require source measures where privacy plays a key role.WSN is used to monitor the dynamic environments and to adapt to such situation sensor networks need Machine Learning techniques to avoid unnecessary redesign. Machine learning techniques survey for WSNs provide a wide range of applications in which security is given top priority. To secure data from attackers the WSNs system should be able to delete the instruction if any hackers/attackers are trying to steal data.
References
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.
Kumar, D. P., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1-25.
Maleh, Y., Ezzati, A., Qasmaoui, Y., & Mbida, M. (2015). A global hybrid intrusion detection system for wireless sensor networks. Procedia Computer Science, 52, 1047-1052.
Ioannis, K., Dimitriou, T., & Freiling, F. C. (2007, April). Towards intrusion detection in wireless sensor networks. In Proc. of the 13th European Wireless Conference (pp. 1-10). Citeseer.
Zhang, W., Han, D., Li, K. C., & Massetto, F. I. (2020). Wireless sensor network intrusion detection system based on MK-ELM. Soft Computing, 1-14.
Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion detection systems in wireless sensor networks: a review. International Journal of Distributed Sensor Networks, 9(5), 167575.
Butun, I., Morgera, S. D., & Sankar, R. (2013). A survey of intrusion detection systems in wireless sensor networks. IEEE communications surveys & tutorials, 16(1), 266-282.
McDermott, C. D., & Petrovski, A. (2017). Investigation of computational intelligence techniques for intrusion detection in wireless sensor networks. International journal of computer networks and communications, 9(4).
Misra, S., Krishna, P. V., & Abraham, K. I. (2010, January). Energy efficient learning solution for intrusion detection in wireless sensor networks. In 2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010) (pp. 1-6). IEEE.
Belavagi, M. C., & Muniyal, B. (2016). Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Computer Science, 89(2016), 117-123.
Tan, X., Su, S., Huang, Z., Guo, X., Zuo, Z., Sun, X., & Li, L. (2019). Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm. Sensors, 19(1), 203.
Amouri, A., Alaparthy, V. T., & Morgera, S. D. (2020). A Machine Learning Based Intrusion Detection System for Mobile Internet of Things. Sensors, 20(2), 461.
Yu, Z., & Tsai, J. J. (2008, June). A framework of machine learning based intrusion detection for wireless sensor networks. In 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008) (pp. 272-279). IEEE.
Abduvaliyev, A., Pathan, A. S. K., Zhou, J., Roman, R., & Wong, W. C. (2013). On the vital areas of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 15(3), 1223-1237.
Soliman, H. H., Hikal, N. A., & Sakr, N. A. (2012). A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks. Egyptian Informatics Journal, 13(3), 225-238.
Roman, R., Zhou, J., & Lopez, J. (2006). Applying intrusion detection systems to wireless sensor networks. In IEEE Consumer Communications & Networking Conference (CCNC 2006).
Loo, C. E., Ng, M. Y., Leckie, C., & Palaniswami, M. (2006). Intrusion detection for routing attacks in sensor networks. International Journal of Distributed Sensor Networks, 2(4), 313-332.
Li, G., He, J., & Fu, Y. (2008). Group-based intrusion detection system in wireless sensor networks. Computer Communications, 31(18), 4324-4332.
Chen, R. C., Hsieh, C. F., & Huang, Y. F. (2009, February). A new method for intrusion detection on hierarchical wireless sensor networks. In Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication (pp. 238-245).
Loo, C. E., Ng, M. Y., Leckie, C., & Palaniswami, M. (2006). Intrusion detection for routing attacks in sensor networks. International Journal of Distributed Sensor Networks, 2(4), 313-332.
Onat, I., & Miri, A. (2005, August). A real-time node-based traffic anomaly detection algorithm for wireless sensor networks. In 2005 Systems Communications (ICW'05, ICHSN'05, ICMCS'05, SENET'05) (pp. 422-427). IEEE.
Hai, T. H., Khan, F., & Huh, E. N. (2007, August). Hybrid intrusion detection system for wireless sensor networks. In International Conference on Computational Science and Its Applications (pp. 383-396). Springer, Berlin, Heidelberg.
Yan, K. Q., Wang, S. C., & Liu, C. W. (2009, March). A hybrid intrusion detection system of cluster-based wireless sensor networks. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1, pp. 18-20).
Wang, Y., Attebury, G., & Ramamurthy, B. (2006). A survey of security issues in wireless sensor networks.
Raymond, D. R., & Midkiff, S. F. (2008). Denial-of-service in wireless sensor networks: Attacks and defenses. IEEE Pervasive Computing, 7(1), 74-81.
Mubarak, T. M., Sattar, S. A., Rao, G. A., & Sajitha, M. (2011, March). Intrusion detection: An energy efficient approach in heterogeneous WSN. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 1092-1096). IEEE.
Islam, M. S., & Rahman, S. A. (2011). Anomaly intrusion detection system in wireless sensor networks: security threats and existing approaches. International Journal of Advanced Science and Technology, 36(1), 1-8.
Singh, S. K., Singh, M. P., & Singh, D. K. (2011). Intrusion detection- based security solution for cluster-based wireless sensor networks. International Journal of Advanced Science and Technology, 30(83).
Jadidoleslamy, H. (2011). A high-level architecture for intrusion detection on heterogeneous wireless sensor networks: hierarchical, scalable and dynamic reconfigurable. Wireless Sensor Network, 3(07), 241.
Zhang, Y., Meratnia, N., & Havinga, P. (2010). Outlier detection techniques for wireless sensor networks: A survey. IEEE communications surveys & tutorials, 12(2), 159-170.