Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models - Gebrekiros Gebreyesus Gebremariam; J. Panda; S. Indu (Alexandria Engineering Journal, 2023)
The identification and localization of malicious nodes in wireless sensor networks (WSNs) is a hot area of research that can considerably extend the network's lifetime and make it more valuable. We use sensors whose positions are known, or anchor nodes, to make educated guesses about the positions of the unknown nodes. Several localization methods have been developed for precise estimation of the unknowable nodes. So, during the network setup process, finding suitable network parameters for node localization with the requisite accuracy in a short amount of time remains a tough task. Due to the fact that they manipulate network resources and routing protocols, routing assaults like wormhole attacks, Sybil attacks, blackhole attacks, and replay attacks are just a few examples of the types of attacks that have the potential to hinder the accuracy of localization and the quality of service provided by WSNs. This work proposes safe localization and detection of routing threats in wireless sensor networks by utilizing hybrid optimized machine learning approaches for optimal distance, position, and data communication. These approaches aim to find the optimal distance between sensors and the optimal position of sensors. Calculating the average localization accuracy and finding malicious nodes both need the use of the benchmark datasets CICIDS2017 and UNSW NB15. The machine learning algorithms that have been provided can be utilized with these datasets. The cluster labelling K-means clustering technique is applied to binary classification in the system that has been proposed. As a consequence, the system achieves an average detection accuracy of 100%. The findings of the simulation indicate that the proposed hybrid strategy is capable of achieving a higher level of localization accuracy of the unknown nodes, with an average localization error of 0.191.