Network Source Identification Mechanism for IoT Devices Using Machine Learning Techniques

Abstract

The rapid progress and evolution of the Internet of Things (IoT) have led to a significant increase in the occurrence of security gaps. Pinpointing the source of network traffic coming from IoT devices can be challenging, but doing so can reduce security risks. This study proposes a network traffic source identification mechanism that leverages machine learning (ML) techniques to accurately determine the source of network traffic. The study utilizes a diverse dataset obtained from a purpose-built IoT/IIoT testbed and employs feature extraction, model development, and evaluation techniques. By utilizing network traffic features, a range of classifiers, including LGBMClassifier (LGBM), CatBoostClassifier (CB), RandomForestClassifier (RF), ExtraTreesClassifier (ET), KneighborsClassifier (KNN), and DecisionTreeClassifier (DT), were trained and evaluated. The results demonstrate exceptional performance across the classifiers, with high accuracy, precision, recall, and F1 scores achieved in identifying the source of network traffic. Among the classifier models, LGBM achieved the best accuracy value of 0.99999857, precision value of 0.99999859, and F1 score of 0.999998803, with CB achieving the best recall of 0.999997875. Some of these results are novel, and others performed better than existing systems. The findings of this study contribute to source identification, ensure the accountability of IoT network users, and provide insights into developing better defenses against security threats in the IoT domain