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With the proliferation of IoT devices, securing the IoT-based network is of paramount importance. IoT-based networks consist of diversely purposed IoT devices. This diversity of IoT devices necessitates diverse dataset analysis to ensure effective implementation of Machine Learning (ML)-based cybersecurity. However, much-demanded real-world IoT data is still in short supply for active ML-based IoT data analysis. This paper presents an in-depth analysis of the real-time IoT-23 dataset [9]. Exhaustive analysis of all 20 scenarios of the IoT-23 dataset reveals the consistency between feature selection methods and detection algorithms. The proposed ML-based intrusion detection system (ML-IDS) achieves significant improvement in detection accuracy, which in some scenarios reaches 100%. Our analysis also demonstrates that the required number of features for a high detection rate of greater than 99% remains small, i.e., 2 or 3, enabling ML-IDS implementation even with resource-constrained IoT processors.