Hierarchical Local Sensitivity Analysis Method for Optimization of Deep Learning-based Anomaly Detection in Communication Network

Deep learning-based anomaly detection techniques in communication network have been widely researched and generally viewed as a classification problem based on the benign data baseline. However, the model deployment on edge devices is urgent to be optimized as compact structure with robust detection...

Full description

Saved in:
Bibliographic Details
Published in2022 IEEE/CIC International Conference on Communications in China (ICCC) pp. 451 - 456
Main Authors Yue, Gang, Sun, Zhuo, Fan, Jinpo
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.08.2022
Subjects
Analytical models
anomaly detection
Communication network
Computational modeling
Deep learning
Integrated circuits
local sensitivity analysis
Neurons
Robustness
robustness improvement
Sensitivity analysis
structure compression
Online AccessGet full text
DOI10.1109/ICCC55456.2022.9880845

Cover

More Information
Summary:Deep learning-based anomaly detection techniques in communication network have been widely researched and generally viewed as a classification problem based on the benign data baseline. However, the model deployment on edge devices is urgent to be optimized as compact structure with robust detection capability due to the constraints of computational and storage capacity. This paper proposed a hierarchical local sensitivity analysis (H-LSA) method to quantify the model's sensitive correlations by deriving the "neuron-layer-model" sensitivity matrices. The method provides a perspective on the sensitivity propagation from the basic unit of the deep learning model, i.e., neuron to the overall model. Furthermore, we demonstrate the applications of the hierarchical sensitivity matrices on CNN-based anomaly detection in the industrial control network communication, which shows the remarkable optimization capabilities of structure compression and robustness improvement.
DOI:10.1109/ICCC55456.2022.9880845