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IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Yan, Yu Qi, Lin Wang, Jie Lin, Yun Chen, Lei Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Georgia Southern Univ Dept Infonnat Technol Coll Engn & Comp Statesboro GA 30458 USA

ISBN: (纸本)9781728150895

Nowadays, network intrusions have brought greater impact in a large scale. Intrusion Detection Systems (IDS) have been a recent research hotspot for both the industry and the academic. However, due to the dynamic characteristics of network traffic, it is challenging to extract significant features and identify the traffic types. This paper focuses on applying deep learning methods to feature extraction. Specifically, an IDS model is proposed based on autoencoder and long short-term memory (LSTM) cell. The overall architecture of the intrusion detection model includes a feature extractor, a classifier, and an evaluation block. Different structures of the feature extraction model have been discussed and researched. Experiments conducted on the UNSW-NB15 dataset produce satisfactory result. A number of selected metrics such as accuracy and false alarm rate are adopted to evaluate the detection performance. Simulation results indicate that our model works better than competing machine learning methods and achieves accuracy of over 92%.

关键词： Feature Reduction autoencoder LSTM Intrusion Detection

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A Novel Inverse Procedure Via Creating TubeNet with Constraint autoencoder for Feature-Space Dimension-Reduction

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INTERNATIONAL JOURNAL OF APPLIED MECHANICS 2021年第8期13卷

作者： Duan, Shuyong Hou, Zhiping Han, Xu Liu, Guirong Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Univ Cincinnati Dept Aerosp Engn & Engn Mech Cincinnati OH 45221 USA

TubeNet has the simplest possible tubular configuration with the uniform number of neurons in all layers and enables explicit inversion. To create a TubeNet, dimension-reduction is a prerequisite for the inverse problems so that the numbers of neurons in the input and output layerscan be made the same. This study introduces a novel procedure to construct inverse TubeNets. The proposed procedure has three major sequential steps. (1) An autoencoder will be used to extract the necessary number of features from a large number of features in a high-dimensional space;(2) Constraints will be imposed to the autoencoder guided by the concepts of the principal component analysis (PCA), so that the extracted features possess the important orthogonality;(3) An L2 regularizer is proposed to adequately impose these constraints on the off-diagonal entries in the weight matrix of the autoencoder, ensuring quality orthogonality. The benchmark problems of inverse identification of material constants of composite laminated plates are used to evaluate the effect of the present TubeNet procedure with the constraint autoencoder, standard autoencoder and PCA, implemented in TubeNet. The study shows that the present constrained autoencoder can effectively overcome the shortcomings of PCA and standard autoencoder, and offers an effective way for dimension-reduction for inverse TubeNet.

关键词： Dimension-reduction autoencoder TubeNet inverse problem feature extraction and fusion deep learning

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Intrusion detection in cyber-physical systems using a generic and domain specific deep autoencoder model

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 91卷 107044-107044页

作者： Thakur, Soumyadeep Chakraborty, Anuran De, Rajonya Kumar, Neeraj Sarkar, Ram Indian Inst Technol Dept Comp Sci & Engn Mumbai 400076 Maharashtra India Jadavpur Univ Dept Comp Sci & Engn 188 Raja SC Mallick Rd Kolkata 700032 India Thapar Inst Engn & Technol Deemed Univ Dept Comp Sci & Engn Patiala Punjab India Univ Petr & Energy Studies Sch Comp Sci Dehra Dun Uttarakhand India Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan

The rapid growth of network-related services in the last decade has produced a huge amount of sensitive data on the internet. But networks are very much prone to intrusions where unauthorized users attempt to access sensitive information and even disrupt the system. Building a competent network intrusion detection system (IDS) is necessary to prevent such attacks. IDSs generally use machine learning algorithms for classifying the attacks. But the features used for classification are not always suitable or sufficient. Besides, the number of intrusions is much less than the number of non-intrusions. Hence naive approaches may fail to provide acceptable performance due to this class imbalance. To counter this problem, in this paper, we propose a model that extracts useful features from the given features and then uses a deep learning algorithm to classify the intrusions. It is to be noted that underlying data points cannot be thought of as sampled from the same distribution, rather from two different distributions - one generic to all network intrusions, and the other specific to the domain. Keeping this fact in mind, we propose a unique Generic-Specific autoencoder architecture where the generic one learns the features that are common across all forms of network intrusions, and the specific ones learn features that are pertaining only to that domain. The model has been evaluated on the CICIDS2017 dataset, which is the largest dataset of this type available online, and we have set new benchmark results on this dataset.

关键词： Intrusion detection Network intrusion CICIDS2017 dataset autoencoder Class imbalance Deep learning

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A novel hybrid framework for wind speed forecasting using autoencoder-based convolutional long short-term memory network

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第11期31卷

作者： Kosana, Vishalteja Madasthu, Santhosh Teeparthi, Kiran Natl Inst Technol Andhra Pradesh Dept Elect Engn Tadepalligudem India Kakatiya Inst Technol & Sci Dept Elect & Elect Engn Warangal 506015 Telangana India

A precise forecast of wind speed is a fundamental requirement of wind power integration. The nonlinear and intermittent nature of the wind makes wind speed forecasting (WSF) complicated for linear approaches. Addressing the complications faced by the linear approaches, this paper proposed a novel and robust approach using long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and LSTM model for enhanced WSF. The proposed hybrid approach is divided into two main components: feature encoding, dimensionality reduction using LSTM autoencoder and forecasting using convolutional LSTM. In the first stage, the LSTM autoencoder eliminates the uncertainties present in raw wind speed data and also reduces the computational load on the forecasting convolutional LSTM approach. Then, in the second stage, CNN is used to extract the optimum features, and the LSTM network is used to forecast the wind speed. Five different benchmark forecasting models are used to evaluate and study the proposed hybrid approach's performance. The experiment is performed with real-time wind speed data from the Garden city wind farm, USA. The proposed hybrid approach performance is verified using various performance metrics. The experimental results demonstrate that the proposed approach improved by 40% over the second best benchmark forecasting approach.

关键词： autoencoder convolutional neural network forecasting long short-term memory model uncertainty wind

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Multi-view Subspace Adaptive Learning via autoencoder and Attention 27th

Multi-view Subspace Adaptive Learning via Autoencoder and At...

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27th International Conference on Neural Information Processing

作者： Liu, Jian-wei Xie, Hao-jie Lu, Run-kun Luo, Xiong-lin China Univ Petr Coll Informat Sci & Engn Dept Automat Beijing Campus Cup Beijing 102249 Peoples R China

ISBN: (纸本)9783030638320;9783030638337

Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clustering (LRSC), cluster the affinity matrix for a single view, thus ignoring the problem of fusion between views. In our article, we propose a new Multiview Subspace Adaptive Learning based on Attention and autoencoder (MSALAA). This method combines a deep autoencoder and a method for aligning the self-representations of various views in Multi-view Low-Rank Sparse Subspace Clustering (MLRSSC), which can not only increase the capability to non-linearity fitting, but also can meets the principles of consistency and complementarity of multi-view learning. We empirically observe significant improvement over existing baseline methods on six real-life datasets.

关键词： Multi-view learning Subspace self-representation autoencoder Attention Spectral clustering

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A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine

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SIGNAL PROCESSING 2021年 181卷 107915-107915页

作者： Khatab, Zahra Ezzati Gazestani, Amirhosein Hajihoseini Ghorashi, Seyed Ali Ghavami, Mohammad Shahid Beheshti Univ Dept Elect Engn Cognit Telecommun Res Grp Tehran Iran Univ East London Sch Architecture Comp & Engn London England London South Bank Univ Sch Engn London England

In recent years, because of the growing demand for location based services in indoor environment and development of Wi-Fi, fingerprint-based indoor localization has attracted many researchers' interest. In Wireless Sensor Networks (WSNs), fingerprint based localization methods estimate the target location by using a pattern matching model for the measurements of the Received Signal Strength (RSS) from the available transmitter sensors, which are collected by a smartphone with internal sensors. Due to the dynamic nature of the environment, the fingerprint database needs to be updated, periodically. Hence, it is better to add new fingerprint data to the primary database in order to update them. However, collecting the labeled data is time consuming and labor intensive. In this paper, we propose a novel algorithm, which uses high level extracted features by an autoencoder to improve the localization performance in the classification process. Furthermore, to update the fingerprint data base, we also add crowd-sourced labeled and unlabeled data in order to improve the localization performance, gradually. Simulation results indicate that the proposed method provides a significant improvement in localization performance, using high level extracted features by the autoencoder, and by increasing the number of unlabeled training data. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Indoor localization Fingerprint Wireless sensor network Semi-supervised autoencoder Deep extreme learning machine

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A spatial-compositional feature fusion convolutional autoencoder for multivariate geochemical anomaly recognition

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COMPUTERS & GEOSCIENCES 2021年 156卷 104890-104890页

作者： Guan, Qingfeng Ren, Shuliang Chen, Lirong Feng, Bin Yao, Yao China Univ Geosci Sch Geog & Informat Engn Wuhan 430078 Hubei Peoples R China China Univ Geosci Natl Engn Res Ctr GIS Wuhan Hubei Peoples R China China Geol Survey Dev & Res Ctr Beijing Peoples R China Chinese Acad Geol Sci Inst Geophys & Geochem Explorat Langfang Hebei Peoples R China Alibaba Grp Hangzhou 311121 Zhejiang Peoples R China

The spatial structural features and compositional relationships of multivariate geochemicals are influenced by complex geological processes (e.g., diagenesis and mineralization), and can help identify geochemical anomalies and provide key references for mineral resource exploration. However, previous machine-learning-based models often treat spatial structural features or compositional relationships separately. Based on the multitask stack autoencoder structure, this study proposes a feature fusion convolutional autoencoder (FCAE) to extract and fuse the spatial structural features and compositional relationships of multivariate geochemicals for identifying geochemical anomalies. In addition, a three-stage training (3ST) strategy combining greedy layerwise pretraining and overall fine-tuning is adopted to calibrate the FCAE. To assess the performance, the proposed FCAE was used to identify the anomalies related to the Cu ore in the southwest area of the Wuyishan polymetallic metallogenic belt in China. The results showed that fusing both spatial structural features and compositional relationships effectively improved the accuracy of the anomaly identification. The FCAE outperformed several existing models by achieving an AUC of 0.863, a recall of 0.909, and the highest intersection point of the P-A plot in the experiments. In addition, the FCAE is less sensitive to the size of the convolution window, which makes the method more applicable and reliable for mineral resource exploration.

关键词： Geochemical anomaly recognition Spatial structural feature Compositional relationships Feature fusion autoencoder

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ASD-SAENet: A Sparse autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2021年 15卷 654315页

作者： Almuqhim, Fahad Saeed, Fahad Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understanding of the neurological mechanisms underlying ASD necessitates the identification of new biomarkers that can aid in study of brain development, and functioning, and can lead to accurate and early detection of ASD. In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features that can be used for classification. These features are then fed into a deep neural network (DNN) which results in superior classification of fMRI brain scans more prone to ASD. Our proposed model is trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error. We evaluated our proposed deep-learning model using publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset collected from 17 different research centers, and include more than 1,035 subjects. Our extensive experimentation demonstrate that ASD-SAENet exhibits comparable accuracy (70.8%), and superior specificity (79.1%) for the whole dataset as compared to other methods. Further, our experiments demonstrate superior results as compared to other state-of-the-art methods on 12 out of the 17 imaging centers exhibiting superior generalizability across different data acquisition sites and protocols. The implemented code is available on GitHub portal of our lab at: https://***/pcdslab/ASD- SAENet.

关键词： ASD fMRI autoencoder sparse autoencoder ABIDE deep-learning classification diagnosis

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Health condition monitoring of machines based on long short-term memory convolutional autoencoder

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APPLIED SOFT COMPUTING 2021年 107卷 107379-107379页

作者： Ye, Zhuang Yu, Jianbo Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

Machine health assessment is crucial to prognostics and health management (PHM), which can increase reliability of machines while reducing operation cost. However, the data collected from machines in real-world cases are noised and high-dimensional, so that it is very difficult to detect the early defects of machines. Moreover, it is still a challenging problem to capture sequential information from multi-sensor signals for machine health assessment. This paper proposes a novel autoencoder (AE), called long short-term memory convolutional autoencoder (LSTMCAE), where LSTM and convolutional units are embedded in this specific network for feature learning from sensor signals based on unsupervised-learning. A long short-term memory (LSTM) unit in LSTMCAE is utilized to capture sequential information from multi-sensor time series data. A convolutional and deconvolutional unit is further embedded after the LSTM unit to filter noise and extract features corresponding to health state of machines. Residual learning is employed to reduce the training difficulty and improve the feature learning performance of LSTMCAE. Multivariate Gaussian distribution (MGD) is adopted to generate health index (HI) based on reconstruction errors of LSTMCAE for quantifying machines health state. A contribution analysis-based feature selection method is proposed to select effective features for machinery health assessment. Experimental results on turbofan engines demonstrate the effectiveness of LSTMCAE for machine health assessment. Compared with other unsupervised learning methods, the HIs generated by LSTMCAE show better tendency. LSTMCAE can detect earlier slight degradation than LSTM-AE. Moreover, contribution analysis results indicate that the sensitive variables can be selected for machine health monitoring. Residual learning significantly improves the feature learning performance of LSTMCAE. Thus, LSTMCAE can well quantify the degradation trend of machines. The experimental results on e

关键词： Machine health assessment Deep learning autoencoder Long short-term memory Convolutional neural network

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UNSUPERVISED LEARNING FROM LIMITED AVAILABLE DATA BY β-NMF AND DUAL autoencoder

UNSUPERVISED LEARNING FROM LIMITED AVAILABLE DATA BY β-NMF ...

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IEEE International Conference on Image Processing (ICIP)

作者： Abukmeil, Mohanad Ferrari, Stefano Genovese, Angelo Piuri, Vincenzo Scotti, Fabio Univ Milan Dept Comp Sci Milan Italy

ISBN: (纸本)9781728163956

Unsupervised Learning (UL) models are a class of Machine Learning (ML) which concerns with reducing dimensionality, data factorization, disentangling and learning the representations among the data. The UL models gain their popularity due to their abilities to learn without any predefined label, and they are able to reduce the noise and redundancy among the data samples. However, generalizing the UL models for different applications including image generation, compression, encoding, and recognition faces different challenges due to limited available data for learning, diversity, and complex dimensions. To overcome such challenges, we propose a partial learning procedure by utilizing the beta-Non Negative Matrix Factorization (beta-NMF), which maps the data into two complementary subspaces constituting generalized driven priors among the data. Moreover, we employ a dual-shallow autoencoder (AE) to learn the subspaces separately or jointly for image reconstruction and visualization tasks, where our model performance shows superior results to the literary works when learning the model with a small amount of data and generalizing it for large-scale unseen data.

关键词： Unsupervised Learning Limited data learning Non-negative Matrix Factorization autoencoder

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