Unsupervised Online Automl for Ddos of Things Prediction by Multimodal Analysis

Distributed Denial of Service (DDoS) of Things are becoming increasingly severe, characterized by unprecedented traffic volumes and rates. Predicting these attacks before they escalate is critical for reducing costs. However, the complex and multidimensional nature of DDoS attacks demands adaptive defense strategies, which are lacking in current approaches. Existing methods often depend on labeled datasets, offline processing or context-specific models, resulting in low prediction accuracy. Further, they tend to rely on homogeneous data sources, limiting their adaptability to the variability of real-world network environments. This paper introduces DELIBERATE, a novel technique for DDoS attack prediction that utilizes unsupervised online AutoML and ordinal pattern transformation through multimodal correlation analysis. DELIBERATE adapts itself to changes in network traffic by leveraging the correlation of heterogeneous data and ordinal patterns transformation, a noisetolerant method suitable for IoT environments. It predicts DDoS attacks up to 47 minutes in advance. The method outperforms traditional approaches that depend on labeled data, extensive training, and complex neural networks.