Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning  

作　　者：Shijie Tang Yong Ding Huiyong Wang 

机构地区：[1]School of Computer Science and Information Security,Guilin University of Electronic Technology,Guilin,541004,China [2]School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin,541004,China [3]Guangxi Engineering Research Center of Industrial Internet Security and Blockchain,Guilin University of Electronic Technology,Guilin,541004,China [4]Institute of Cyberspace Technology,HKCT Institute for Higher Education,Hong Kong,999077,China [5]School of Mathematics&Computing Science,Guilin University of Electronic Technology,Guilin,541004,China

出　　处：《Computers, Materials & Continua》2025年第1期1129-1150,共22页计算机、材料和连续体（英文）

基　　金：supported in part by the Guangxi Science and Technology Major Program under grant AA22068067;the Guangxi Natural Science Foundation under grant 2023GXNSFAA026236 and 2024GXNSFDA010064;the National Natural Science Foundation of China under project 62172119.

摘　　要：As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关 键 词：Anomaly detection CPS deep learning MLP(multi-layer perceptron) 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]