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21st IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI)

作者： Huang, Sheng-Tai Kang, Yihuang Hung, Shao-Min Kuo, Bowen Cheng, I-Ling Natl Sun Yat Sen Univ Dept Informat Management Kaohsiung Taiwan Natl Chung Hsing Univ Grad Inst Lib & Informat Sci Taichung Taiwan

ISBN: (纸本)9781728110547

Researchers have been overwhelmed by the explosion of research articles published by various research communities. Many research scholarly websites, search engines, and digital libraries have been created to help researchers identify potential research topics and keep up with recent progress on research of interests. However, it is still difficult for researchers to keep track of the research topic diffusion and evolution without spending a large amount of time reviewing numerous relevant and irrelevant articles. In this paper, we consider a novel topic diffusion discovery technique. Specifically, we propose using a deep Non-negative autoencoder with information divergence measurement that monitors evolutionary distance of the topic diffusion to understand how research topics change with time. The experimental results show that the proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.

关键词： deep autoencoder Online Machine Learning Topic Modeling Topic Diffusion Topic Detection and Tracking

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deep autoencoders as anomaly detectors: Method and case study in a distributed water treatment plant

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COMPUTERS & SECURITY 2020年 99卷 102055-102055页

作者： Raman, Mr Gauthama Dong, Wenjie Mathur, Aditya Singapore Univ Technol & Design Ctr Cyber Secur Res iTrust Singapore Singapore

Industrial Control Systems (ICS) are found in critical infrastructure, such as, water treatment plants and oil refineries. ICS are often the target of cyber-attacks leading to undesirable consequences. It is essential to detect process anomalies resulting from such attacks before appropriate defensive actions are considered. In this work, a deep autoencoder-based anomaly detector (DAE) is proposed. DAE is trained using data collected during normal operation of a plant. The detection effectiveness of three variants of DAE was experimentally evaluated on an operational Secure Water Treatment (SWaT) plant. Further, the amount of plant design knowledge needed to design DAE was compared with that needed to create design-centric approaches for anomaly detection. Experimental results indicate that the proposed DAE, constructed with minimal design knowledge, is effective in detecting process anomalies re sulting due to single and multi-point coordinated attacks with high detection rate and few false alarms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Anomaly Detection deep autoencoder cyber-attacks Cyber-Physical Systems Water Treatment Plant

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Attribute graph anomaly detection utilizing memory networks enhanced by multi-embedding comparison

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NEUROCOMPUTING 2025年 633卷

作者： Zhang, Lianming Wu, Baolin Dong, Pingping Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China

In complex attribute networks, accurately pinpointing anomalous nodes is vital for grasping network behavior and safeguarding network security. Traditional anomaly detection methods often struggle to fully harness the intricate relationships that underpin attributes and structures, thus curbing their practical effectiveness. To transcend this limitation, we introduce a novel graph anomaly detection model that harmoniously integrates node attributes and structural information. Our model employs multi-embedding contrast modules, coupled with memory network enhancements, to pinpoint anomalous nodes. Precisely, we crafted a multi-embedding contrast module to encode the attributes and structures inherent within nodes, generating a multitude of embedding representations. By scrutinizing the discrepancies between these representations, our model adeptly identifies nodes that deviate from attribute and structural consistency, indicating anomalies. Furthermore, we incorporate a memory network to reconstruct node attributes, thereby enhancing the attribute decoding process while preserving the straightforwardness of structural decoding. To validate our method, we conducted extensive experiments on five authoritative public graph datasets, comparing various graph anomaly detection methods using rigorous metrics such as AUC, precision, and recall. The experimental results unequivocally demonstrate that our proposed method surpasses current state-of-the-art techniques in detecting anomalous nodes within graphs, solidly validating its efficacy.

关键词： Anomaly detection Attributed networks deep autoencoder Social networks

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Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder

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BRIEFINGS IN BIOINFORMATICS 2022年第3期23卷 bbac104-bbac104页

作者： Liu, Wei Lin, Hui Huang, Li Peng, Li Tang, Ting Zhao, Qi Yang, Li Xiangtan Univ Sch Comp Sci Sch Cyberspace Sci Xiangtan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Tsinghua Univ Acad Arts & Design Beijing Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China Xiangtan Univ Sch Publ Adm Xiangtan Peoples R China

Increasing evidences show that the occurrence of human complex diseases is closely related to microRNA (miRNA) variation and imbalance. For this reason, predicting disease-related miRNAs is essential for the diagnosis and treatment of complex human diseases. Although some current computational methods can effectively predict potential disease-related miRNAs, the accuracy of prediction should be further improved. In our study, a new computational method via deep forest ensemble learning based on autoencoder (DFELMDA) is proposed to predict miRNA-disease associations. Specifically, a new feature representation strategy is proposed to obtain different types of feature representations (from miRNA and disease) for each miRNA-disease association. Then, two types of low-dimensional feature representations are extracted by two deep autoencoders for predicting miRNA-disease associations. Finally, two prediction scores of the miRNA-disease associations are obtained by the deep random forest and combined to determine the final results. DFELMDA is compared with several classical methods on the The Human microRNA Disease Database (HMDD) dataset. Results reveal that the performance of this method is superior. The area under receiver operating characteristic curve (AUC) values obtained by DFELMDA through 5-fold and 10-fold cross-validation are 0.9552 and 0.9560, respectively. In addition, case studies on colon, breast and lung tumors of different disease types further demonstrate the excellent ability of DFELMDA to predict disease-associated miRNA-disease. Performance analysis shows that DFELMDA can be used as an effective computational tool for predicting miRNA-disease associations.

关键词： miRNA-disease association prediction deep forest ensemble learning deep autoencoder feature representation

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Optimisation of sparse deep autoencoders for dynamic network embedding

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1361-1376页

作者： Huimei Tang Yutao Zhang Lijia Ma Qiuzhen Lin Liping Huang Jianqiang Li Maoguo Gong College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina Institute for Infocomm Research Agency for Science Technology and ResearchSingaporeSingapore Key Laboratory of Collaborative Intelligence Systems Ministry of EducationXidian UniversityXi'anChina

Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters.A sparse deep autoencoder(called SPDNE)for dynamic NE is proposed,aiming to learn the network structures while preserving the node evolution with a low computational *** tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic ***,an adaptive simulated algorithm to find the optimal sparse architecture for the deep autoencoder is *** performance of SPDNE over three dynamical NE models(*** architecture-based deep autoencoder method,DynGEM,and ElvDNE)is evaluated on three well-known benchmark networks and five real-world *** experimental results demonstrate that SPDNE can reduce about 70%of weight parameters of the architecture for the deep autoencoder during the training process while preserving the performance of these dynamical NE *** results also show that SPDNE achieves the highest accuracy on 72 out of 96 edge prediction and network reconstruction tasks compared with the state-of-the-art dynamical NE algorithms.

关键词： deep autoencoder dynamic networks low-dimensional feature space network embedding sparse structure

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EMI signal feature enhancement based on extreme energy difference and deep auto-encoder

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IET SIGNAL PROCESSING 2018年第7期12卷 852-856页

作者： Li, Hongyi Chen, Shengyu Xu, Shaofeng Song, Ziming Chen, Jiaxin Zhao, Di Beihang Univ Sch Math & Syst Sci LMIB Beijing 100191 Peoples R China Beihang Univ Sch Software Beijing 100191 Peoples R China Fudan Univ Sch Comp Sci & Technol Shanghai 201203 Peoples R China New York Univ Abu Dhabi Dept Elect & Comp Engn Abu Dhabi U Arab Emirates

To enhance features of different electromagnetic interference (EMI) signals, which are significant for further feature extraction and pattern recognition, the authors propose an EMI signal feature enhancement method based on extreme energy difference and a deep auto-encoder. Experimental results show that this method can effectively enhance features of EMI signals and improve recognition accuracy.

关键词： signal resolution electromagnetic interference encoding extreme energy difference deep autoencoder electromagnetic interference feature extraction pattern recognition EMI signal feature enhancement method

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Distributed Framework for Detecting PMU Data Manipulation Attacks With deep autoencoders

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IEEE TRANSACTIONS ON SMART GRID 2019年第4期10卷 4401-4410页

作者： Wang, Jingyu Shi, Dongyuan Li, Yinhong Chen, Jinfu Ding, Hongfa Duan, Xianzhong Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China

Phasor measurement unit (PMU) data manipulation attacks (PDMAs) may blind the control centers to the real-time operating conditions of power systems. Detecting these attacks accurately is essential to ensure the normal operation of power system monitoring and control. Using long-term accumulated historical PMU measurements to train a machine learning model to detect PDMAs has shown promising results. In this paper, deepautoencoder-based anomaly measurers are deployed throughout the power system to build a distributed PDMA detection framework. The architecture of a deep autoencoder and its training process are introduced. How to convert the historical PMU measurements into data samples for learning is also elaborated upon. Once trained, an anomaly measurer can assess the PDMA existence possibility of the new PMU measurements. By integrating the results of different anomaly measurers, the proposed distributed PDMA detection framework can detect PDMAs in the whole power system. The effectiveness and detection performance of the framework are discussed through experiments.

关键词： Cyber-physical security deep autoencoder distributed detection framework PMU data manipulation attack

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Network intrusion detection system for DDoS attacks in ICS using deep autoencoders

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WIRELESS NETWORKS 2024年第6期30卷 5059-5075页

作者： Ortega-Fernandez, Ines Sestelo, Marta Burguillo, Juan C. Pinon-Blanco, Camilo Galician Res & Dev Ctr Adv Telecommun GRADIANT Carretera Vilar 56-58 Vigo 36214 Spain Univ Vigo Dept Stat & OR SiDOR Res Grp Vigo 36310 Spain CITMAga Santiago De Compostela 15782 Spain Univ Vigo Escola Enxenaria Telecomunicac Vigo 36310 Spain Univ Vigo atlanTTic Res Ctr Vigo 36310 Spain

Anomaly detection in industrial control and cyber-physical systems has gained much attention over the past years due to the increasing modernisation and exposure of industrial environments. Current dangers to the connected industry include the theft of industrial intellectual property, denial of service, or the compromise of cloud components;all of which might result in a cyber-attack across the operational network. However, most scientific work employs device logs, which necessitate substantial understanding and preprocessing before they can be used in anomaly detection. In this paper, we propose a network intrusion detection system (NIDS) architecture based on a deep autoencoder trained on network flow data, which has the advantage of not requiring prior knowledge of the network topology or its underlying architecture. Experimental results show that the proposed model can detect anomalies, caused by distributed denial of service attacks, providing a high detection rate and low false alarms, outperforming the state-of-the-art and a baseline model in an unsupervised learning environment. Furthermore, the deep autoencoder model can detect abnormal behaviour in legitimate devices after an attack. We also demonstrate the suitability of the proposed NIDS in a real industrial plant from the alimentary sector, analysing the false positive rate and the viability of the data generation, filtering and preprocessing procedure for a near real time scenario. The suggested NIDS architecture is a low-cost solution that uses only fifteen network-based features, requires minimal processing, operates in unsupervised mode, and is straightforward to deploy in real-world scenarios.

关键词： Network intrusion detection system Anomaly detection Industrial control systems Cyber-physical systems Industrial cybersecurity deep autoencoder

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Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study

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HUMAN BRAIN MAPPING 2019年第3期40卷 944-954页

作者： Pinaya, Walter H. L. Mechelli, Andrea Sato, Joao R. Univ Fed ABC Ctr Math Comp & Cognit Rua Arcturus 03 BR-09606070 Sao Bernardo Do Campo SP Brazil Univ Fed ABC Ctr Engn Modeling & Appl Social Sci Sao Bernardo Do Campo SP Brazil Kings Coll London Inst Psychiat Psychol & Neurosci Dept Psychosis Studies London England

Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a black box that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as deep autoencoder to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n =263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p <.005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.

关键词： autism spectrum disorder computational psychiatry deep autoencoder deep learning schizophrenia structural MRI

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Learning binary codes for fast image retrieval with sparse discriminant analysis and deep autoencoders

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INTELLIGENT DATA ANALYSIS 2023年第3期27卷 809-831页

作者： Hong, Son An Huu, Quynh Nguyen Viet, Dung Cu Thuy, Quynh Dao Thi Quoc, Tao Ngo Viet Hung Univ Hanoi Vietnam Thuyloi Univ Hanoi Vietnam Posts & Telecommun Inst Technol Hanoi Vietnam Vietnam Acad Sci & Technol Inst Informat Technol Hanoi Vietnam

Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant analysis to select the most important original feature set, and solve the small class problem in the relevance feedback. Besides, to increase the retrieval performance on large-scale image databases, in addition to BCFIR mapping real-valued features to short binary codes, it also applies a bagging learning strategy to improve the ability general capabilities of autoencoders. In addition, our proposed method also takes advantage of both labeled and unlabeled samples to improve the retrieval precision. The experimental results on three databases demonstrate that the proposed method obtains competitive precision compared with other state-of-the-art image retrieval methods.

关键词： Content-based image retrieval (CBIR) sparse discriminant analysis deep autoencoder binary code

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