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

With rapid technological growth, security attacks are drastically increasing. In many crucial Internet-of-Things (IoT) applications such as healthcare and defense, the early detection of security attacks plays a significant role in protecting huge resources. An intrusion detection system is used to address this problem. The signature-based approaches fail to detect zero-day attacks. So anomaly-based detection particularly AI tools, are becoming popular. In addition, the imbalanced dataset leads to biased results. In Machine Learning (ML) models, the $F_{1}$ score is an important metric to measure the accuracy of class-level correct predictions. The model may fail to detect the target samples if the $F_{1}$ is considerably low. It will lead to unrecoverable consequences in sensitive applications such as healthcare and defense. So, any improvement in the $F_{1}$ score has a significant impact on the resource protection. In this paper, we present a framework for an ML-based intrusion detection system for an imbalanced dataset. In this study, the most recent dataset, namely CICIoT2023 is considered. The random forest (RF) algorithm is used in the proposed framework. The proposed approach improves 3.72%, 3.75% and 4.69% in precision, recall and $F_{1}$ score, respectively, with the existing method. Additionally, for unsaturated classes (i.e., classes with $F_{1}$ score < 0.99), $F_{1}$ score improved significantly by 7.9%. As a result, the proposed approach is more suitable for IoT security applications for efficient detection of intrusion and is useful in further studies.