Effective Multi-Stage Training Model For Edge Computing Devices In
  Intrusion Detection
Thua Huynh Trong Thanh Nguyen Hoang
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.