Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection
This work addresses the problem of adapting intrusion detection for edge computing devices, but it is incremental as it builds on existing deep learning approaches with specific optimizations.
The paper tackled the challenge of deploying intrusion detection models on resource-constrained edge devices by introducing a three-stage training paradigm with pruning and compression, achieving model size reduction while maintaining accuracy comparable to similar methods on the UNSW-NB15 dataset.
Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accuracy of intrusion detection models still need further enhancement to render them more adaptable to diverse system categories, particularly within resource-constrained devices, such as those embedded in edge computing systems. This research introduces a three-stage training paradigm, augmented by an enhanced pruning methodology and model compression techniques. The objective is to elevate the system's effectiveness, concurrently maintaining a high level of accuracy for intrusion detection. Empirical assessments conducted on the UNSW-NB15 dataset evince that this solution notably reduces the model's dimensions, while upholding accuracy levels equivalent to similar proposals.