Detections of IoT Attacks via Machine Learning-Based Approaches with Cooja
DOI:
https://doi.org/10.4108/eetiot.v7i28.324Keywords:
IoT security, Attacks, Machine Learning-based approaches, Decision tree-based models, Cooja simulatorAbstract
Once hardware becomes "intelligent", it is vulnerable to threats. Therefore, IoT ecosystems are susceptible to a variety of attacks and are considered challenging due to heterogeneity and dynamic ecosystem. In this study, we proposed a method for detecting IoT attacks that are based on ML-based approaches that release the final decision to detect IoT attacks. However, we have implemented three attacks as a sample in the IoT via Contiki OS to generate a real dataset of IoT-based features containing a mix of data from malicious nodes and normal nodes in the IoT network to be utilized in the ML-based models. As a result, the multiclass random decision forest ML-based model achieved 98.9% overall accuracy in detecting IoT attacks for the real novel dataset compared to the decision tree jungle, decision forest tree regression, and boosted decision tree regression, which achieved 87.7%, 93.2%, and 87.1%, respectively. Thus, the decision tree-based approach efficiently manipulates and analyzes the KoÜ-6LoWPAN-IoT dataset, generated via the Cooja simulator, to detect inconsistent behavior and classify malicious activities.
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