Multi-Label Classification for Drift Detection in IoT Data Streams - Althabiti, Mashail; Abdullah, Manal; Almatrafi, Omaima (Communications in Mathematics and Applications, 2024)
With the prevalence of Internet of Things (IoT) systems, data is exponentially growing, resulting in data streams. Data streams are massive, potentially non-stop, and arrive at high speed. These systems process IoT data streams in a non-stationary manner, making them susceptible to concept drift occurrence and class imbalance. Concept drift occurs as a result of the change in the underlying distribution over time, while class imbalance occurs when some class distribution is uneven. This paper proposes an Implicit Drift Detection model with Multi-Label kNN (IDD-MLkNN), aimed at addressing concept drift in multi-label classification for IoT data streams. While the model is applicable across various domains, its performance was specifically assessed using two IoT datasets — Bot_IoT and Edge_IIoTset — associated with intrusion detection systems. Applying the proposed model to oversee IoT network traffic offers practical advantages, potentially reducing the time and expenses of re-examining attack data. The evaluation was conducted for sudden and gradual concept drift scenarios. Experimental results show the superiority of the IDD-MLkNN over other well-known multilabel classification models in different performance measures such as the Subset Accuracy, Accuracy, Hamming Score, and F-measure. However, it was less efficient in terms of Evaluation Time compared to other multi-label classification methods.