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Computers, Materials & Continua 2023年第1期74卷 1749-1763页

作者： Hyo-Seok Kim Chang-Gyoon Lim Sang-Joon Lee Yong-Min Kim Interdisciplinary Program of Information Security Chonnam National UniversityGwangju61186Korea Major in Computer Engineering Chonnam National UniversityYeosu59626Korea School of Business Administration Chonnam National UniversityGwangju61186Korea Department of Electronic Commerce Chonnam National UniversityYeosu59626Korea

Recently,Industrial Control Systems(ICSs)have been changing from a closed environment to an open environment because of the expansion of digital transformation,smart factories,and Industrial Internet of Things(IIoT).Since security accidents that occur in ICSs can cause national confusion and human casualties,research on detecting abnormalities by using normal operation data learning is being actively *** single technique proposed by existing studies does not detect abnormalities well or provide satisfactory *** this paper,we propose a GRU-based Buzzer Ensemble for abnormaldetection(GBE-AD)model for detecting anomalies in industrial control systems to ensure rapid response and process *** newly proposed ensemble model of the buzzer method resolves False Negatives(FNs)by complementing the limited range that can be detected in a single model because of the internal models composing *** the internal models remain suppressed for False Positives(FPs),GBE-AD provides better *** addition,we generated mean prediction error data in GBE-AD and inferred abnormal processes using soft and hard *** confirmed that the detection model’s Time-series Aware Precision(TaP)suppressed FPs at 97.67%.The final performance was 94.04%in an experiment using anHIL-basedAugmented ICS(HAI)Security Dataset(ver.21.03)among public datasets.

关键词： Industrial control system abnormal detection ensemble learning HAI dataset

Collective Representation for abnormal Event detection

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Journal of Computer Science & Technology 2017年第3期32卷 470-479页

作者： Renzhen Ye Xuelong Li Center for Optical Imagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an 710119 China School of Electronics and Information Engineering Xi'an Jiaotong University Xi'an 710119 China

abnormal event detection in crowded scenes is a hot topic in computer vision and information retrieval community. In this paper, we study the problems of detecting anomalous behaviors within the video, and propose a robust collective representation with multi-feature descriptors for abnormal event detection. The proposed method represents different features in an identical representation, in which different features of the same topic will show more common properties. Then, we build the intrinsic relation between different feature descriptors and capture concept drift in the video sequence, which can robustly discriminate between abnormal events and normal events. Experimental results on two benchmark datasets and the comparison with the state-of-the-art methods validate the effectiveness of our method.

关键词： abnormal detection collective representation dictionary learning

SOINN-Based abnormal Trajectory detection for Efficient Video Condensation

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Computer Systems Science & Engineering 2022年第8期42卷 451-463页

作者： Chin-Shyurng Fahn Chang-Yi Kao Meng-Luen Wu Hao-En Chueh National Taiwan University of Science and Technology Taipei City106335Taiwan Soochow University Taipei City100Taiwan Tamkang University New Taipei City251301Taiwan Chung Yuan Christian University Taoyuan City32023Taiwan

With the evolution of video surveillance systems,the requirement of video storage grows rapidly;in addition,safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal *** most of the scene in the surveillance video are redundant and contains no information needs attention,we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of *** goal is to improve the condensation rate to reduce more storage size,and increase the accuracy in abnormal *** the trajectory feature is the key to both goals,in this paper,a new method for feature extraction of moving object trajectory is proposed,and we use the SOINN(Self-Organizing Incremental Neural Network)method to accomplish a high accuracy abnormal *** the results,our method is able to shirk the video size to 10%storage size of the original video,and achieves 95%accuracy of abnormal event detection,which shows our method is useful and applicable to the surveillance industry.

关键词： Surveillance systems video condensation SOINN moving trajectory abnormal detection

A generative model of identifying informative proteins from dynamic PPI networks

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Science China(Life Sciences) 2014年第11期57卷 1080-1089页

作者： ZHANG Yuan CHENG Yue JIA KeBin ZHANG AiDong Department of Electrical Information and Control Engineering Beijing University of Technology Department of Computer Science and Engineering State University of New York at BuffaloBuffalo NY 14260-2500 USA

Informative proteins are the proteins that play critical functional roles inside *** are the fundamental knowledge of translating bioinformatics into clinical *** methods of identifying informative biomarkers have been developed which are heuristic and arbitrary,without considering the dynamics characteristics of biological *** this paper,we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks(PPINs).In this model,the common representation of multiple PPINs is learned using a deep feature generation model,based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative *** were implemented on data of yeast cell cycles and different prostate cancer *** analyze the effectiveness of reconstruction by comparing different methods,and the ranking results of informative proteins were also compared with the results from the baseline *** method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.

关键词： dynamic protein-protein interaction network abnormal detection multi-view data deep belief network

Anomaly detection and Pattern Differentiation in Monitoring Data from Power Transformers

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Energy Engineering 2022年第5期119卷 1811-1828页

作者： Jun Zhao Shuguo Gao Yunpeng Liu QuanWang Ziqiang Xu Yuan Tian Lu Sun State Grid Hebei Electric Power Research Institute Shijiazhuang050021China Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense North China Electric Power UniversityBaoding071003China State Key Laboratory of Alternate Electrical Power Systemwith Renewable Energy Sources North China Electric Power University Beijing 102206 China

Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change,external environmental interference,communication interruption,and other factors,a method of anomaly recognition and differentiation for monitoring data was ***,the empirical wavelet transform(EWT)and the autoregressive integrated moving average(ARIMA)model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value,and then the isolation forest algorithm was used to identify the abnormal information,and the monitoring sequence was segmented according to the recognition ***,the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method,and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors,and the monitoring sequence correlation was further used to verify the ***,the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.

关键词： abnormal detection empirical wavelet transform autoregressive integrated moving average isolated forest

Wavelet analysis method for detection of DDoS attack on the basis of self-similarity

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Frontiers of Electrical and Electronic Engineering in China 2007年第1期2卷 73-77页

作者： REN Xunyi WANG Ruchuan WANG Haiyan College of Computer Nanjing University of Posts and TelecommunicationsNanjing 210003China

As the traditional methods were not suitable for the detection of small distribute denial of service(DDoS)attack and identification of busy traffic,on the basis of the influence of DDoS attack,one wavelet analysis method was *** method of coefficient variance analysis was deduced and a software model for the method was *** addition,key issues of the choice of wavelet and calculation of Hurst were *** experimental results show that the proposed method has more advantages in accurately identifying busy traffic and detection of small DDoS attack.

关键词： abnormal detection distribute denial of service self-similarity wavelet transform

Application on Anomaly detection of Geoelectric Field Based on Deep Learning

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Earthquake Research in China 2020年第3期34卷 358-377页

作者： WEI Lei AN Zhanghui FAN Yingying CHEN Quan YUAN Lihua HOU Zeyu Lanzhou Institute of Seismology China Earthquake AdministrationLanzhou 730000China Lanzhou Geophysics National Observation and Research Station Lanzhou 730000China Gansu Earthquake Agency Lanzhou 730000China School of Science Lanzhou University of TechnologyLanzhou 730050China

The deep learning method has made nurnerials achievements regarding anomaly detection in the field of time *** introduce the speech production model in the field of artificial intelligence,changing the convolution layer of the general convolution neural network to the residual element structure by adding identity mapping,and expanding the receptive domain of the model by using the dilated causal *** on the dilated causal convolution network and the method of log probability density function,the anomalous events are detected according to the anomaly *** validity of the method is verified by the simulation data,which is applied to the actual observed data on the observation staion of Pingliang geoeletric field in Gansu *** results show that one month before the Wenchuan M_S8.0,Lushan M_S7.0 and Minxian-Zhangxian M_S6.6 earthquakes,the daily cumulative error of log probability density of the predicted results in Pingliang Station suddenly decreases,which is consistent with the actual earthquake anomalies in a certain time *** analyzing the combined factors including the spatial electromagnetic environment and the variation of micro fissures before the earthquake,we explain the possible causes of the anomalies in the geoelectric field of before the *** successful application of deep learning in observed data of the geoelectric field may behefit for improving the ultilization rate both the data and the efficiency of ***,it may provide technical support for more seismic research.

关键词： Deep learning Time series Dilated causal convolution Geoelectric field abnormal detection

A Method for Detecting abnormality of CAN Bus in Vehicle

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Instrumentation 2017年第2期4卷 28-33页

作者： PENG Jing ZHANG Zhihong HE Hong School of Electrical and Electronic Engineering Tianjin University of TechnologyTianjin Key Laboratory of Complex System Control Theory and Application Transmission and Transmission Department Tianjin Radio and Television Station

With the development of intelligent and netw orking technology in automobile,the malicious attacks against in-vehicle CAN netw orks are increasing day by day,and the problem of information safety in automobile is aggravated. In this regard,this paper analyzes the security loopholes and threats w hich the CAN bus faced,put forw ard a kind of anomaly detection algorithm for vehicle CAN bus. The method uses support vector machine algorithm to distinguish betw een normal message and abnormal message,so as to realize the CAN bus anomaly detection. Theoretical and experimental studies show that this method can effectively detect abnormal packets in the CAN bus w ith a detection rate of over 90%,w hich can effectively resist malicious attacks such as tampering and cheating on the vehicle CAN bus.

关键词： Automobile CAN bus Information Security Support Vector Machines abnormal detection