Intrusion detection technique in wireless sensor network using grid search random forest with Boruta feature selection algorithm

Attacks in wireless sensor networks (WSNs) aim to prevent or eradicate the network's ability perform its anticipated functions. Intrusion detection is a defense used that can detect unknown attacks. Due incredible development computer-related applications and massive Internet usage, it indispensable provide host network security. The of hacking technology tries compromise computer security through intrusion. system (IDS) was employed with help machine learning (ML) Algorithms intrusions network. Classic ML algorithms like support vector (SVM), K-nearest neighbour (KNN), filter-based feature selection often led poor accuracy misclassification intrusions. This article proposes novel framework for IDS be enabled by Boruta grid search random forest (BFS-GSRF) algorithm overcome these issues. performance BFS-GSRF compared linear discriminant analysis (LDA) classification regression tree (CART) etc. proposed work implemented tested on laboratory — knowledge discovery dataset (NSL-KDD). experimental results show model yields higher (i.e., 99%) detecting attacks, superior LDA, CART, other existing algorithms.