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An automated intrusion detection system in IoT system using Attention based Deep Bidirectional sparse Auto Encoder model

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KNOWLEDGE-BASED SYSTEMS 2024年 305卷

作者： Swathi, K. Bindu, G. Hima GITAM Deemed Univ Dept Comp Sci & Engn Hyderabad 502329 Telangana India GITAM Sch Technol Dept Comp Sci & Engn Hyderabad 502329 Telangana India

Nowadays, the Internet of Things (IoT) is a smart network connected to the Internet for transmitting gathered data with verified protocols. Attackers frequently use communication protocol defects as the basis for their attacks. Better protection measures are required since attacks affect the reputations of service providers. Both machine learning (ML) and deep learning (DL) methods have been developed in a number of research works to detect network intrusions. However, the system's security is limited by the rising number of new threats. Critical problems in IoT platforms, cyber-physical systems, wireless networks, and fog computing are caused by such attacks. The development of various cyber-security attacks reinforces the need for a strong intrusion detection system (IDS) in the IoT platform. The proposed study introduced a robust deep-feature learning mechanism for automatically detecting network intruders in the IoT platform. Initially, input data are gathered from the given dataset. Pre-processing helps reduce any noise in the data and improves the data quality using cleaning, outlier removal, and min-max normalization. The proposed Attention-based Deep Bidirectional sparse Auto Encoder (AD-BiSA) model is the most important feature retrieved using the attention-based deep Bi-LSTM model. The different IoT network threats are categorized using a sparse autoencoder approach. The chaotic Seagull Optimization (CSGO) algorithm decreases the loss and enhances the weight in the proposed DL technique. The UNSW NB15_IDS and NSL-KDD datasets achieve accuracy rates of 99.71% and 98.97%, respectively, for the proposed technique. The proposed method achieves better performance than existing approaches.

关键词： IoT attacks Automated intrusion detection system Attention-based Deep Bi-LSTM sparse autoencoder Chaotic Seagull Optimization

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Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and sparse Auto Encoder Method

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Computers, Materials & Continua 2023年第7期76卷 793-811页

作者： Deepthi K.Oommen J.Arunnehru Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalaniChennai600026TamilnaduIndia

Alzheimer’s Disease(AD)is a progressive neurological *** diagnosis of this illness using conventional methods is very *** Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast *** disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age *** light of research investigations,it is vital to consider age as one of the key criteria when choosing the *** younger subjects are more susceptible to the perishable side than the older *** proposed investigation concentrated on the younger *** research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages *** proposed work is executed in three *** 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)*** Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of *** model was trained and tested to classify the five stages of *** ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.

关键词： Alzheimer’s disease mild cognitive impairment Weiner filter contrast limited adaptive histogram equalization transfer learning sparse autoencoder deep neural network

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Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2018年 99卷 459-477页

作者： Ahmed, H. O. A. Wong, M. L. D. Nandi, A. K. Brunel Univ London Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England Heriot Watt Univ Malaysia Precinct 5 Putrajaya 62200 Malaysia Tongji Univ Coll Elect & Informat Engn Key Lab Embedded Syst & Serv Comp Shanghai Peoples R China

Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Compressed sensing sparse over-complete representations Deep neural network sparse autoencoder Bearing fault classification Machine condition monitoring

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A Generative Model for sparse Hyperparameter Determination

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IEEE TRANSACTIONS ON BIG DATA 2018年第1期4卷 2-10页

作者： Wan, Zhiqiang He, Haibo Tang, Bo Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

sparse autoencoder is an unsupervised feature extractor and has been widely used in the machine learning and data mining community. However, a sparse hyperparameter has to be determined to balance the trade-off between the reconstruction error and the sparsity of sparse autoencoder. Traditional sparse hyperparameter determination method is time-consuming, especially when the dataset is large. In this paper, we derive a generative model for sparse autoencoder. Based on this model, we derive a formulation to determine the sparse hyperparameter effectively and efficiently. The relationship between the sparse hyperparameter and the average activation of sparse autoencoder hidden units is also presented in this paper. Experimental results and comparative studies over numerous datasets demonstrate the effectiveness of our method to determine the sparse hyperparameter.

关键词： sparse autoencoder sparse hyperparameter feature extractor generative model

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A sparse FEATURE REPRESENTATION FOR GENETIC DATA ANALYSIS 14

A SPARSE FEATURE REPRESENTATION FOR GENETIC DATA ANALYSIS

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Liu, Hua-Hao Huang, Pei-Jie Lin, Pi-Yuan Lin, Wen-Hu Qi, Pei-Heng Song, Chong-Hua South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China

ISBN: (纸本)9781467372206

Feature representation is one of the key research issues in machine learning. In some applications with high dimensionality of data, e.g. genomic microarray data, obtaining a good feature representation with effective dimensionality reduction still remains a challenge. In this paper, instead of selecting a subset of original features by feature selection, we use sparse autoencoder to find a reconstructed feature representation for genetic data analysis. The performance of our proposed method is empirically evaluated using one of the genomic microarray dataset provided in ASU Feature Selection Repository. The results show that the proposed method yield better classification accuracy than some representative feature selection methods.

关键词： Feature representation sparse autoencoder Machine learning Genetic data analysis

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Patch-based sparse and Convolutional autoencoders for Anomaly Detection in Hyperspectral Images 28

Patch-based Sparse and Convolutional Autoencoders for Anomal...

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28th Iranian Conference on Electrical Engineering (ICEE)

作者： Rezvanian, Amir Reza Imani, Maryam Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172965

Anomaly target detection is one of the major aims of Hyperspectral Image (HSI) processing. Since anomalous pixels Compose a small fraction of the hyperspectral data cube, the use of supervised neural networks presents many complications. The reason is that supervised networks need a large training set to fine-tune the model. In this paper we propose two semi-supervised autoencoder based anomaly detection methods using the reconstruction error of each pixel. The reconstruction error is the mean absolute error between each pixel and its reconstruction by the proposed autoencoder networks. The proposed networks are deep fully-connected sparse autoencoders (SAE) and deep one-dimensional convolutional autoencoders (CAE). In addition, a patch-based anomaly detection method is proposed which takes spatial correlation between neighbouring pixels into account. We use the San Diego airport hyperspectral data to carry out the experiments. The results are compared with some state-of-the-art HSI anomaly detection methods. Quantitative results employing ROC and AUC metrics demonstrate the superior performance of the proposed method compared to several anomaly detectors.

关键词： anomaly detection convolutional autoencoder hyperspectral images sparse autoencoder

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Fuzzy Rule Reduction using sparse Auto-Encoders

Fuzzy Rule Reduction using Sparse Auto-Encoders

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Sevakula, Rahul K. Verma, Nishchal K. Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India

ISBN: (纸本)9781467374286

Fuzzy Rule based regression, classification and control have found great use in modern applications due to its simplicity, flexibility and capability. A key issue in all such methods is the computation time. Computational complexity of training and testing is linearly dependent on the size of fuzzy rule base and the respective fuzzy rule space is exponentially dependent on data dimensionality. sparse Auto-Encoders (SAs) have become popular in giving compact feature representations for image, audio and speech data and have helped in giving state of the art pattern recognition performances in most of the domains. These feature representation are learnt in an unsupervised fashion and are found to give higher order building blocks with which the data is seemingly made of. This paper proposes a method where SAs are used for getting compact feature representation of input data and if needed with reduced dimensionality. The regular fuzzy rule based models are then learnt from data in the new feature space. The method was tested for Regression and Classification problems, giving impressive results in both. The method with Regression problem gave comparable performance with almost half the number of rules and with Classification problem it gave improvement in classification accuracy by 2.67% while reducing the size of fuzzy rule base by 11.25 times and 7.5 times by number.

关键词： sparse autoencoder fuzzy rule reduction deep learning deep belief networks dimensionality reduction

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Feature Abstraction for Early Detection of Multi-type of Dementia with sparse Auto-encoder

Feature Abstraction for Early Detection of Multi-type of Dem...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Alkabawi, Elham M. Hilal, Allaa R. Basir, Otman A. Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781538616451

With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective strategy to enable early diagnosis of dementia. Nevertheless, the early diagnosis of dementia is a challenging task due to the high dimensionality of MRI scans that may degrade the effectiveness of machine learning models. Feature abstraction is a part of dimensionality reduction process need by researchers to represent input data in its simplest form that results in a more-robust system model. It is a technique that collects relevant features and ignores irrelevant or redundant ones from data without loss of much key information. This paper proposes a novel feature abstraction method using sparse autoencoder (SAE) to reduce the dimensionality of and extract key features from MRI neuroimages. These features and that obtained from the popular PCA approach are then used to train a Linear Discriminant Analysis (LDA) and Logistic Regression classifiers in order to compare their prediction accuracy. The experimental results show that the proposed approach yields higher classification accuracy compared to that using the PCA by 8 percent of classification accuracy. The experimental results show that the use of features learned by SAE in early diagnosis of multi-type dementia provides better classification performance than the use of raw image pixel intensities for diagnosing dementia.

关键词： Dimensionality Reduction Early diagnosis of Dementia Feature abstraction Machine learning PCA sparse autoencoder

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DETECTION OF LUMEN AND MEDIA-ADVENTITIA BORDERS IN IVUS IMAGES USING sparse AUTO-ENCODER NEURAL NETWORK 14

DETECTION OF LUMEN AND MEDIA-ADVENTITIA BORDERS IN IVUS IMAG...

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IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Su, Shengran Gao, Zhifan Zhang, Heye Lin, Qiang Hau, William Kongto Li, Shuo Zhejiang Univ Technol Coll Sci Hangzhou Zhejiang Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Beijing Peoples R China Univ Hong Kong LiKaShing Fac Med Hong Kong Hong Kong Peoples R China Univ Western Ontario London ON Canada

ISBN: (纸本)9781509011728

This paper describes an artificial neural network (ANN) method that employs a feature-learning algorithm to detect the lumen and MA borders in intravascular ultrasound (IVUS) images. Three types of imaging features including spatial, neighboring, and gradient features were used as the input features to the neural network, and then the different vascular layers were distinguished using two sparse autoencoders and one softmax classifier. To smooth the lumen and MA borders detected by the ANN method, we used the active contour model. The performance of our approach was compared with the manual drawing method and another existing method on 538 IVUS images from six subjects. Results showed that our approach had a high correlation (r = 0.9284 +/- 0.9875 for all measurements) and good agreement (bias = 0.0148 +/- 0.4209 mm) with the manual drawing method, and small detection error (lumen border: 0.0928 +/- 0.0935 mm, MA border: 0.1056 +/- 0.1088 mm). The average time to process each image was 14 +/- 4.6 seconds. The obtained results indicate that our proposed approach can be used to efficiently and accurately detect the lumen and MA borders in IVUS images.

关键词： Intravascular image Deep neural network Image segmentation sparse autoencoder

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Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer Science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target *** key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain ***,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain ***,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain *** address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called *** capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature *** higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain *** addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local *** results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

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