Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection

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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Thua Huynh Trong, Thanh Nguyen Hoang
Format: Article
Language:English
Subjects:
Accuracy
Edge computing
Training
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2401.17546