Machine Learning Models for Malicious Traffic Detection in IoT Networks/IoT-23 Dataset/

Connected devices are penetrating markets with an unprecedented speed. Networks that carry Internet of Things (IoT) traffic need highly adaptable tools for traffic analysis to detect and suppress malicious agents. This has prompted researchers to explore the various benefits Machine Learning (ML) has to offer. By developing models to detect certain kinds of malicious traffic accurately, ML approach will allow for better detection capabilities if implemented in an Intrusion Detection System (IDS) or next-generation firewalls. This research paper focuses on harnessing features of ML in exploring the network traffic generated by infected IoT devices. The IoT-23 dataset was used and preprocessed into three different datasets for further exploration using various ML algorithms. This enhances the detection of malicious traffic, thereby improving the security in the IoT ecosystem. The ML algorithms implemented in this paper included: Logistic Regression, Decision Tree, Random Forest Classifier, XGBoost and Artificial Neural Network. This research was able to achieve almost 100% accuracy across all the three sub-datasets.