| Summary: | Security attacks are becoming more sophisticated and common as connected devices rapidly exchange personal, sensitive, and important data. Security solutions are therefore required for Internet of Things (IoT) environments. System administrators receive alerts through an automatic Network Intrusion Detection (NID) system when security breaches occur. An automatic NID can be an effective tool to protect IoT networks against various attacks. It is possible to detect intrusions using a variety of intrusion detection techniques, but the performance and class imbalance in the dataset make this a difficult process. To improve detection rates and decrease false alarms, intrusion detection accuracy must be improved. In this paper, an automatic NID system is proposed leveraging a renowned machine learning model named Random Forest (RF) on the (UNSW-NB15) dataset collected from Kaggle. The experimental results indicate that the proposed model not only has higher accuracy at 90.17% surpassing the baseline approach by 7.34%, but also has precision, recall, and F1 scores up to 90.14%, 90.17, and 90.14%, respectively. Moreover, 98.83% accuracy is achieved with a balanced class dataset by using random resampling techniques to generate synthetic data of minority attacks.
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