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An increase in cyber threats directed at interconnected devices has resulted from the proliferation of the Internet of Things (IoT), which necessitates the implementation of comprehensive defenses against evolving attack vectors. This research investigates the utilization of machine learning (ML) prediction models to identify and defend against cyber-attacks targeting IoT networks. Central emphasis is placed on the thorough examination of the CIC-IoT2023 dataset, an extensive collection comprising a wide range of Distributed Denial of Service (DDoS) assaults on diverse IoT devices. This ensures the utilization of a practical and comprehensive benchmark for assessment. This study develops and compares four distinct machine learning models Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) to determine their effectiveness in detecting and preventing cyber threats to the Internet of Things (IoT). The comprehensive assessment incorporates a wide range of performance indicators, such as F1-score, accuracy, precision, and recall. Significantly, the results emphasize the superior performance of DT and RF, demonstrating exceptional accuracy rates of 0.9919 and 0.9916, correspondingly. The models demonstrate an outstanding capability to differentiate between benign and malicious packets, as supported by their high precision, recall, and F1 scores. The precision-recall curves and confusion matrices provide additional evidence that DT and RF are strong contenders in the field of IoT intrusion detection. Additionally, KNN demonstrates a noteworthy accuracy of 0.9380. On the other hand, LR demonstrates the least accuracy with a value of 0.8275, underscoring its inherent incapability to classify threats. In conjunction with the realistic and diverse characteristics of the CIC-IoT2023 dataset, the study's empirical assessments provide invaluable knowledge for determining the most effective machine learning algorithms and fortification strategies to protect IoT infrastructures. Furthermore, this study establishes ground-breaking suggestions for subsequent inquiries, urging the examination of unsupervised learning approaches and the incorporation of deep learning models to decipher complex patterns within IoT networks. These developments have the potential to strengthen cybersecurity protocols for Internet of Things (IoT) ecosystems, reduce the impact of emergent risks, and promote robust defense systems against ever-changing cyber challenges.
Jony et al. (Thu,) studied this question.