Enhancing Security in Wireless Sensor Networks: Related Approaches in Intruder Detection Techniques

Authors

  • M. Supriya, T. Adilakshmi

Keywords:

Intrusion Detection Systems (IDS), Wireless Sensor Networks (WSN), Secure Histogram Inclination Boosting Classifier

Abstract

WSNs are vulnerable to a number of security threats, each of which has the potential to lower the network's overall performance. The characteristics of distributed, infrastructure-less, fault-tolerant, scalable, and dynamic wireless sensor networks (WSNs) define them A WSN's Intruder Detection System (IDS) is an essential component that contributes to the network's security and integrity. The need for a reliable and secure IDS has grown increasingly important as a result of the growing reliance on WSNs in a variety of applications, including healthcare, the military, and industrial. Although secure routing protocols, key management, and authentication mechanisms ensure safe transmission, there is no assurance that messages will be delivered consistently. To put it another way, these strategies are capable of protecting the network from outside threats, but they are ineffective against inside threats. They want to make certain that the data are true, that they are accurate, and that they are private. These techniques disguise delicate data in case of an attack from an external perspective, when a foe endeavors to gain admittance to the information. An inside assault happens when a sensor hub that is coordinated into the sensor network starts acting in a threatening way without first endeavoring to gain admittance to the data that is remembered for the messages that have been gotten.

Downloads

Download data is not yet available.

References

Safaldin, Mukaram, Mohammed Otair, and Laith Abualigah. "Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks." Journal of ambient intelligence and humanized computing 12 (2021): 1559-1576.

Singh, Abhilash, Jaiprakash Nagar, Sandeep Sharma, and Vaibhav Kotiyal. "A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks." Expert Systems with Applications 172 (2021): 114603.

Sinha, Somnath, and Aditi Paul. "Neuro-fuzzy based intrusion detection system for wireless sensor network." Wireless personal communications 114 (2020): 835-851.

Sood, Tanya, Satyartha Prakash, Sandeep Sharma, Abhilash Singh, and Hemant Choubey. "Intrusion detection system in wireless sensor network using conditional generative adversarial network." Wireless Personal Communications 126, no. 1 (2022): 911-931.

Poornima, I. Gethzi Ahila, and B. Paramasivan. "Anomaly detection in wireless sensor network using machine learning algorithm." Computer communications 151 (2020): 331-337.

Zhang, Wenjie, Dezhi Han, Kuan-Ching Li, and Francisco Isidro Massetto. "Wireless sensor network intrusion detection system based on MK-ELM." Soft Computing 24 (2020): 12361-12374.

Han, Lansheng, Man Zhou, Wenjing Jia, Zakaria Dalil, and Xingbo Xu. "Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model." Information sciences 476 (2019): 491-504.

Boubiche, Djallel Eddine, Samir Athmani, Sabrina Boubiche, and Homero Toral-Cruz. "Cybersecurity issues in wireless sensor networks: current challenges and solutions." Wireless Personal Communications 117 (2021): 177-213.

Premkumar, M., and T. V. P. Sundararajan. "DLDM: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks." Microprocessors and Microsystems 79 (2020): 103278.

Prithi, S., and S. Sumathi. "LD2FA-PSO: A novel learning dynamic deterministic finite automata with PSO algorithm for secured energy efficient routing in wireless sensor network." Ad Hoc Networks 97 (2020): 102024.

Almaiah, Mohammed Amin. "A new scheme for detecting malicious attacks in wireless sensor networks based on blockchain technology." In Artificial Intelligence and Blockchain for Future Cybersecurity Applications, pp. 217-234. Cham: Springer International Publishing, 2021.

Beheshtiasl, Azam, and Ali Ghaffari. "Secure and trust-aware routing scheme in wireless sensor networks." Wireless Personal Communications 107 (2019): 1799-1814.

Sharma, H.; Haque, A.; Blaabjerg, F. Machine learning in wireless sensor networks for smart cities: A survey. Electronics 2021, 10, 1012. [CrossRef]

Schwendemann, S.; Amjad, Z.; Sikora, A. A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines. Comput. Ind. 2021, 125, 103380. [CrossRef]

Liu, H.; Lang, B. Machine learning and deep learning methods for intrusion detection systems: A survey. Appl. Sci. 2019, 9, 4396. [CrossRef]

Cui, L.; Yang, S.; Chen, F.; Ming, Z.; Lu, N.; Qin, J. A survey on application of machine learning for Internet of Things. Int. J. Mach. Learn. Cybern. 2018, 9, 1399–1417. [CrossRef] [17] Rezaee, A.A.; Pasandideh, F. A Fuzzy Congestion Control Protocol Based on Active Queue Management in Wireless Sensor Networks with Medical Applications. Wirel. Pers. Commun. 2018, 98, 815–842. [CrossRef] [18]. Masdari, M. Energy Efficient Clustering and Congestion Control in WSNs with Mobile Sinks; Springer: Berlin/Heidelberg, Germany, 2020; Volume 111, ISBN 0123456789.

Sangeetha, G.; Vijayalakshmi, M.; Ganapathy, S.; Kannan, A. A heuristic path search for congestion control in WSN. Lect. Notes Netw. Syst. 2018, 11, 485–495. [CrossRef] [20]. Chen, S.; Wen, H.; Wu, J.; Chen, J.; Liu, W.; Hu, L.; Chen, Y. Physical-Layer Channel Authentication for 5G via Machine Learning Algorithm. Wirel. Commun. Mob. Comput. 2018, 2018, 6039878. [CrossRef]

Downloads

Published

05.06.2024

How to Cite

M. Supriya. (2024). Enhancing Security in Wireless Sensor Networks: Related Approaches in Intruder Detection Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 4285–4292. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6143

Issue

Section

Research Article