Intrusion Detection System For Wireless Sensor Networks: A Machine Learning Based Approach

Abstract

wireless Sensor Networks (WSNs) are critical for a variety of monitoring applications, but they are prone to
security concerns such as unauthorized access, attacks, and other malicious behavior, which can jeopardize their
reliability. To mitigate these concerns, using Intrusion Detection systems (IDS) is critical for early detection and
response. several datasets, such as KDD Cup data, NSL KDD, u.s.a.-NB 15, and AWID, are often used to train
and assess IDS models. feature selection is an vital step in improving the performance of these fashions, and
strategies like SelectKBest paired with the ANOVA F-take a look at offer effective feature reduction and increased
accuracy. the use of these datasets and feature selection methods, the paper analyzes the use of a Stacking
Classifier strategy that mixes Bagging with Random forest and Boosting with decision Tree algorithms. This
approach achieves high accuracy throughout all datasets tested, presenting a complete strategy to the troubles
faced by safety risks in WSNs. The findings highlight the price of ensemble processes in optimizing IDS
performance for stepped forward security in WSNs.