Concept Drift-Resilient IoT Botnet Detection

This paper addresses a critical challenge in deploying AI-based IoT security solutions: concept drift. The proposed framework offers a scalable and adaptive approach that avoids continuous retraining, a common bottleneck in dynamic environments. The use of latent space representation learning, alignment models, and graph neural networks is a promising combination for robust detection. The focus on real-world datasets and experimental validation strengthens the paper's contribution.

• •Addresses concept drift in IoT botnet detection.
• •Proposes a framework that avoids continuous classifier retraining.
• •Utilizes latent space representation learning, alignment models, and graph neural networks.
• •Evaluated on real-world heterogeneous IoT traffic datasets.