Concept Drift Analysis by Dynamic Residual Projection for effectively Detecting Botnet Cyber-attacks in IoT scenarios

We propose a technique featuring a dynamic sliding window based on the residual projection to perform concept drift analysis. During the process of finding concepts in data streams, the sample number is updated dynamically by comparing the anomalous quantity obtained by the residual projection method in the current window to the previous one. In addition to the Bot-IoT dataset, our method is also applied to two popular synthetic datasets SEA Concept and UG-2C-5D. The results demonstrate the effectiveness of our method with respect to the false alarm rate, misses, and average delay.