Abstract:
In order to identify and categorize network traffic, we developed an Intrusion Detection System
(IDS) model in this study that is based on supervised machine learning techniques. We prepared the dataset for supervised learning techniques. The type of data resolution, imbalance,
and missing values are the main characteristics of the preparation of the dataset. The dataset
is presented in terms of network layers and the characteristic selection uses Random Forest
(RF) form. After that, the network and transport cluster is created. To develop a four-model,
four machine learning algorithms are applied to the provided dataset, which consists of all lay ers. The model was constructed with the aid of Artificial Neural Network (ANN), RF, Support
Vector Machine (SVM), and Naive Bayes (NB). We conducted the studies using multi-class
classification, layer clusters, complete features, and significant features. The evaluation of the
experiments is important to see the accuracy, processing time, and parameter tuning to calculate
the production stage performance of the algorithm. The findings show that Random Forest is
the most effective choice for adjusting accuracy and efficiency, particularly in tasks centered on
network communication; in addition to being more diverse and achieving the highest accuracy
when compared to state-of-the-art, it also modifies the IoT-23 dataset to lower computational
complexity and resource consumption. It is anticipated that this study will further the field of
IoT network intrusion detection. The achieved accuracy depends on different algorithms and
clusters and is from 58 to 78; with highest accuracy Random Forest has been suggested. The
time taken by classification is also s hown; with the lowest t ime, Naive Bayes is not only the
most efficient process but also the fastest.
