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The internet of things (IoT) has revolutionized connectivity and introduced significant security challenges. In this context, intrusion detection systems (IDS) play a crucial role in detecting attacks in IoT environments. Bot-IoT datasets often face class imbalance issues, with the attack class having significantly more samples than the normal class. Addressing this imbalance is essential to enhance IDS performance. The study evaluates various techniques, including imbalance ratio techniques we call imbalance ratio formula (IRF) for controlling imbalance data, while also testing IRF to compare it with oversampling techniques like synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). This research also incorporates the extreme gradient boosting (XGBoost) ensemble model approach to improve IDS performance in dealing with multiclass imbalance issues in Bot-IoT datasets. Through in-depth analysis, we identify the strengths and weaknesses of each method. This study aims to guide researchers and practitioners working on IDS in high-risk IoT environments. The proposed IRF, when integrated with the XGBoost algorithm has been demonstrated to achieve comparable accuracy of 99.9993% while reducing the training time to be on average at least two times faster than those achieved by the other state-of-the-art ensemble methods. [ABSTRACT FROM AUTHOR]

The rapid development of the internet of things (IoT) has taken an important role in daily activities. As it develops, IoT is very vulnerable to attacks and creates IoT for users. Intrusion detection system (IDS) can work efficiently and look for activity in the network. Many data sets have already been collected, however, when dealing with problems involving big data and hight data imbalances. This article proposes, using the dataset used by BotIoT to evaluate the system framework to be created, the XGBoost model to improve the detection performance of all types of attacks, to control unbalanced data using the imbalance ratio of each class weight (CW). The experimental results show that the proposed approach greatly increases the detection rate for infrequent disturbances. [ABSTRACT FROM AUTHOR]