B-DT Model: A Derivative Ensemble Method to Improve Performance of Intrusion Detection System

Abstract

In cyber security, system security must be prioritized. Therefore, to improve system security, a system device called an Intrusion Detection System (IDS) is needed. IDS is a system that can detect suspicious activity on a system or network. The constraint of IDS is many types of attacks appear now, making it difficult to detect them. Therefore, many IDS based on machine learning have been applied to overcome this constraint. And machine learning has been widely adopted to improve IDS performance. However, false detection occurs frequently. The problem raised in this study is the large number of false detections that still occur. The main objective of this study is to reduce the occurrence of false detection in IDS. Then, to achieve this objective, this paper proposes a model called the B-DT model. The Bagging-DT (B-DT) model combines the Bagging technique ensemble-base and Decision Tree (DT) classifier. The B-DT model was trained and evaluated on NSL-KDD and UNSW-NB15 datasets. The results showed that it can reduce false detection from 11,305 data to 243 data in the NSL-KDD dataset. Besides that, the B-DT model can reduce false detection from 2,504 data to 871 in the UNSW-NB15 dataset. In addition, model performance has increased in accuracy, precision, recall, f1-score, and kappascore. Based on the results, the B-DT model's performance can achieve an accuracy of 99.45% on the NSL-KDD dataset and 79.67% on the UNSW-NB15 dataset. This model can work well not only on binary-class data but also on multi-class labeled data. The statistical evaluation shows this model has increased significantly compared to other models. These results suggest that the proposed B-DT model can effectively enhance the performance of IDS and be a promising solution for practical applications.

Text Mining Algorithm Naive Bayes Classifier to Improve Quality Sentiment Analysis Passport Mobile Application

“…The process of data mining or data mining at the KDD stage can also be called text mining or text mining, because the data that researchers use is in the form of text, then at this stage, the Naive Bayes classifier algorithm is implemented for sentiment analysis of Mobile Passport application reviews (Amarudin et al, 2024). This stage is carried out after the data has passed the sentiment class labeling, this process is carried out by classification analysis, namely by making training data and testing data, then classifying with the Naive Bayes classifier algorithm and visualizing barplots and word clouds of the positive and negative words produced.…”