Journal:IEEE Transactions on Network and Service Management

Impact Factor:5.4

Summary:As the confrontation of the industrial cybersecurity upgrades, multi-dimensional variables measured by the SCADA multi-sensor are critical for assessing security risks in industrial field devices. While Deep Learning (DL) methods based on generative models have demonstrated effectiveness, the impact of missing features in samples and temporal window size on modeling and detection processes has been consistently overlooked. To address these challenges, this work proposes an IMADP framework that integratively solves two tasks of missingness patching and anomaly detection. Firstly, the Window-based Adaptive Selection Strategy (WASS) is also designed to intelligently window samples, reducing reliance on prior settings. Secondly, an imputer is constructed under WASS to restore sample integrity, which is implemented by a fully-connected network centered on Neural Controlled Differential Equations (NCDEs). Thirdly, a adversarial diffusion detection model with the variant Transformer as the inverse solver is proposed. Additionally, the Adaptive Dynamic Mask Mechanism (ADMM) is built upon to bolster the model’s comprehension of inter-dependencies between time and sensor nodes. Simultaneously, adversarial training is introduced to optimize training and detection latency caused by the excessive diffusion step size during the native Conditional Diffusion process. The experimental results validate that the proposed framework has the capability to build detectors using missing training samples, and its overall detection performance, tested across six datasets, is superior to existing methods.

Key Words:SCADA Multi-sensor , Anomaly Detection , Imputation-based , Conditional Diffusion

Indexed by:SCI JCR Q1

Note:https://ieeexplore.ieee.org/abstract/document/11419166

Discipline:Engineering

Document Type:JCR 一区

Translation or Not:No