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With rapid technological growth, security attacks are drastically increasing.In many crucial Internet-of-Things (IoT) applications such as healthcare anddefense, the early detection of security attacks plays a significant role inprotecting huge resources. An intrusion detection system is used to addressthis problem. The signature-based approaches fail to detect zero-day attacks.So anomaly-based detection particularly AI tools, are becoming popular. Inaddition, the imbalanced dataset leads to biased results. In Machine Learning(ML) models, F1 score is an important metric to measure the accuracy ofclass-level correct predictions. The model may fail to detect the targetsamples if the F1 is considerably low. It will lead to unrecoverableconsequences in sensitive applications such as healthcare and defense. So, anyimprovement in the F1 score has significant impact on the resource protection.In this paper, we present a framework for ML-based intrusion detection systemfor an imbalanced dataset. In this study, the most recent dataset, namelyCICIoT2023 is considered. The random forest (RF) algorithm is used in theproposed framework. The proposed approach improves 3.72%, 3.75% and 4.69% inprecision, recall and F1 score, respectively, with the existing method.Additionally, for unsaturated classes (i.e., classes with F1 score < 0.99), F1score improved significantly by 7.9%. As a result, the proposed approach ismore suitable for IoT security applications for efficient detection ofintrusion and is useful in further studies.

IIDS: Design of Intelligent Intrusion Detection System for Internet-of-Things Applications