INTRUSION DETECTION SYSTEM USING MACHINE LEARNING

Abstract

This research presents a new method for improving the effectiveness and precision of intrusion detection
systems (IDS) within the framework of dynamic wireless networks. The suggested approach seeks to maximize intrusion
detection and classification by fusing Principal Component Analysis (PCA) with the Random Forest classification
algorithm. By reducing the dimensionality of the dataset, PCA efficiently organizes and streamlines the data in preparation
for more in-depth research. The Random Forest algorithm is then applied for classification, making use of its ensemble
learning properties to raise intrusion detection accuracy.
It is shown that the suggested strategy is effective through extensive testing and analysis. PCA and
Random Forest-based IDS perform better than traditional methods like Support Vector Machines (SVM),
Naive Bayes, and Decision Trees, according to comparison analysis. Remarkably, the suggested approach
surpasses previous approaches with an accuracy percentage of 96.78%. Furthermore, the approach
demonstrates efficacy in real-time intrusion detection settings, underscoring its practical application, with a
processing time of 3.24 minutes.
All things considered, this research represents a major breakthrough in network security, providing a
strong defense against the growing difficulties brought on by cyberattacks. The suggested intrusion detection
system (IDS) improves accuracy and guarantees fast intrusion detection and mitigation by combining PCA for
data preprocessing and Random Forest for classification. This strengthens the resilience of wireless
communication networks against malicious-activities.