Enhancing Intrusion Detection System on Nodemcu With Ensemble Voting

Abstract

As part of the Internet, a NodeMCU becomes a vulnerability to cyber-attack, so the intrusion detection system (IDS) against cyber-attack on the NodeMCU becomes a poignant research challenge. Several studies have applied several machine learning techniques for IDS on NodeMCU; however, there is a research opportunity to improve the performance of existing models. This research aims to increase the IDS performance on NodeMCU with ensemble voting. We hypothesize the realization of how the implementation of an IDS in NodeMCU. Then we obtain and observe a dataset from Kaggle. The dataset comprises five attacks: misconfiguration, DDoS, probe, scanning, and MiTM. Then we design the detection with ensemble voting consisting of a decision tree and a random forest. We benchmark our proposed solution with decision trees and random forest performance. This study uses several test metrics, including , , , and .The test results show that the decision tree has better Precision in predicting misconfiguration attacks and scan attacks than random forests. On the other hand, the random forest has better in predicting normal data, DDoS attacks, probe attacks, and MiTM attacks. In terms of , ensemble voting has the best performance, which is , compared to the decision tree and random forest, which are and , respectively. We conclude that by assembling a decision tree in the random forest with ensemble voting, random forest performance can improve.