Optimized Ensemble Deep Learning for Real-Time Intrusion Detection on Resource-Constrained Raspberry Pi Devices

The rapid growth of Internet of Things (IoT) networks has increased security risks, making it essential to have effective Intrusion Detection Systems (IDS) for real-time threat detection. Deep learning techniques offer promising solutions for such detection due to their superior complex pattern recognition and anomaly detection capabilities in large datasets. This paper proposes an optimized ensemble-based IDS designed specifically for efficient deployment on edge hardware. However, deploying such computationally intensive models on resource-limited edge devices remains a significant challenge due to model size and computational overhead on devices with limited processing capabilities. Building upon our previously developed stacked Long Short-Term Memory (LSTM) model integrated with ANOVA feature selection, we optimize it by integrating dual-stage model compression: pruning and quantization to create a lightweight model suitable for real-time inference on Raspberry Pi devices. To evaluate the system under realistic conditions, we combined with a Kafka-based testbed to simulate dynamic IoT environments with variable traffic loads, delays, and multiple simultaneous attack sources. This enables the assessment of detection performance under varying traffic volumes, latency, and overlapping attack scenarios. The proposed system maintains high detection performance with accuracy of 97.3% across all test scenarios, while efficiently leveraging multi-core processing with peak CPU usage reaching 111.8%. These results demonstrate the system’s practical viability for real-time IoT security at the edge.