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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Singh, Yashika Rathi, Ritika Prasad, Rahul Baghel, R.K. Maulana Azad National Institute of Technology Department of ECE Madhya Pradesh Bhopal India

ISBN: (纸本)9798350304480

Wireless sensor networks (WSNs) are extensively deployed to gather and process data from monitoring environments. Due to their deployment in harsh and unattended conditions, sensor nodes are highly susceptible to faults, which can have severe consequences on safety, economy, and system reliability. To tackle this challenge, machine learning algorithms have emerged as a promising solution. In this study, we propose a fault detection algorithm based on an autoencoder-based classification model. Unlike traditional neural networks, autoencoders have equal numbers of neurons in the input and output layers, facilitating fault detection by comparing input and output values. We evaluate the proposed algorithm against existing approaches using three key parameters: fault detection accuracy, false alarm rate, and false positive rate. Our simulation results demonstrate the superior performance of the proposed algorithm in detecting faults such as spike, fixed bias, gain, and out-of-bounds faults within the network. © 2023 IEEE.

关键词： autoencoder fault detection Sensor nodes (SN) soft faults wireless sensor network (WSN)

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Uncertainty-autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation

Uncertainty-Autoencoder-Based Privacy and Utility Preserving...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Mandal, Bishwas Amariucai, George Wei, Shuangqing Kansas State Univ Dept Comp Sci Manhattan KS 66506 USA Louisiana State Univ Div Elect & Comp Engn Baton Rouge LA 70803 USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class, while under data-type ignorant conditions the categorical variables are represented by a collection of scores, one for each class. We use a neural network architecture consisting of a generator and a discriminator, where the generator consists of an encoder-decoder pair, and the discriminator consists of an adversary and a utility provider. Unlike previous research considering this kind of architecture, which leverages autoencoders (AEs) without introducing any randomness, or variational autoencoders (VAEs) based on learning latent representations which are then forced into a Gaussian assumption, our proposed technique introduces randomness and removes the Gaussian assumption restriction on the latent variables, only focusing on the end-to-end stochastic mapping of the input to privatized data. We test our framework on different datasets: MNIST, FashionMNIST, UCI Adult, and US Census Demographic Data, providing a wide range of possible private and utility attributes. We use multiple adversaries simultaneously to test our privacy mechanism - some trained from the ground truth data and some trained from the perturbed data generated by our privacy mechanism. Through comparative analysis, our results demonstrate better privacy and utility guarantees than the existing works under similar, datatype ignorant conditions, even when the latter are considered under their original restrictive single-adversary model.

关键词： data privacy utility min-max optimization autoencoder categorical features data-type-aware privacy

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Healthcare security in cloud-based wireless sensor networks: Botnet attack detection via autoencoder-aided goal-based artificial intelligent agent

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第19期36卷 e8152-e8152页

作者： Amirthabai, Subasini Chellathurai Malhotra, Udita Rajeswari, Saravanan Thapasimuthu Natesan, Sudhahar Thachankurichy St Josephs Inst Technol Dept Comp Sci & Engn Chennai India Guru Jambheshwar Univ Sci & Technol Dept Comp Sci & Engn Hisar Haryana India SRM Inst Sci & Technol Dept Computat Intelligence Kattankulathur India Rajalakshmi Inst Technol Dept Artificial Intelligence & Data Sci Chennai India

This paper presents a new method in the field of healthcare security that specifically targets cloud-based wireless sensor networks (WSNs). The suggested method integrates a goal-based artificial intelligent agent (GAIA) with an autoencoder (AE) architecture, yielding an autoencoder-based agent (AE-A). The main goal of this integrated system is to improve the efficiency of identifying botnet assaults, with a specific emphasis on the evolving security threats related to cloud computing. Our concept is around creating a meticulously calibrated, goal-driven AI agent tailored explicitly for healthcare applications. The agent meticulously analyses network data and proficiently integrates autoencoder-enhanced anomaly detection techniques to uncover intricate patterns indicative of botnet activities. The adaptability of the goal-based AI agent is improved by ongoing real-time learning, guaranteeing that its responses are in line with the primary goal of neutralizing threats. The autoencoder serves a vital role in the system by functioning as a tool for extracting features. This approach enables the AI Agent to navigate complex information and derive significant insights efficiently. Cloud computing resources greatly enhance the functionalities of a system, enabling scalability, real-time analysis, and improved responsiveness. Utilizing goal-driven AI and autoencoder together proves to be a successful strategy in safeguarding healthcare-oriented WSNs against botnet attacks. This technique takes a proactive stance in ensuring the security of sensitive medical data. The suggested model is evaluated against various models, including the bidirectional long short-term memory (BLSTM) method, the hybrid BLSTM with recurrent neural network (BLSTM-RNN) algorithm, and the Random Forest algorithm. The models are evaluated using metrics such as Matthews correlation coefficient (MCC), prediction rate, accuracy, recall, precision, and F1 score analysis. The investigation demonstrates tha

关键词： autoencoder autoencoder-based agent (AE-A) botnet attack detection cloud-based WSNs goal-based artificial intelligent agent (GAIA) healthcare security

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Feature Encoding with autoencoder and Differential Evolution for Network Intrusion Detection using Machine Learning 22

Feature Encoding with Autoencoder and Differential Evolution...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Leon, Miguel Markovic, Tijana Punnekkat, Sasikumar Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden

ISBN: (纸本)9781450392686

With the increasing use of computer networks and distributed systems, network security and data privacy are becoming major concerns for our society. In this paper, we present an approach based on an autoencoder trained with differential evolution for feature encoding of network data with the goal of improving security and reducing data transfers. One of the novel elements used in differential evolution for intrusion detection is the enhancements in the fitness function by adding the performance of a machine learning algorithm. We conducted an extensive evaluation of six machine learning algorithms for network intrusion detection using encoded data from well-known publicly available network datasets UNSW-NB15. The experiments clearly showed the supremacy of random forest, support vector machine, and K-nearest neighbors in terms of accuracy, and this was not affected to a high degree by reducing the number of features. Furthermore, the machine learning algorithm that was used during training (Linear Discriminant Analysis classifier) got a 14 percentage points increase in accuracy. Our results also showed clear improvements in execution times in addition to the obvious secure aspects of encoded data. Additionally, the performance of the proposed method outperformed one of the most commonly used feature reduction methods, Principal Component Analysis.

关键词： Differential Evolution Neural Networks autoencoder Machine Learning Intrusion Detection

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Fooling intrusion detection systems using adversarially autoencoder

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Digital Communications and Networks 2021年第3期7卷 453-460页

作者： Junjun Chen Di Wu Ying Zhao Nabin Sharma Michael Blumenstein Shui Yu College of Information Science and Technology Beijing University of Chemical TechnologyBeijing100029China School of Computer Science University of Technology SydneyUltimo2007Australia Centre for Artificial Intelligence University of Technology SydneyUltimo2007Australia

Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network *** existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network *** having been used in the real world widely,the above methods are vulnerable to some types of *** this paper,we propose a novel attack framework,Anti-Intrusion Detection autoencoder(AIDAE),to generate features to disable the *** the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,***,a generative adversarial network is used to learn the flexible prior distribution of the latent *** correlation between continuous and discrete features can be kept by using the proposed training *** conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.

关键词： Intrusion detection Cyber attacks autoencoder Generative adversarial networks

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Prediction of optical properties of oxide glass combined with autoencoder and machine learning

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JOURNAL OF NON-CRYSTALLINE SOLIDS 2024年 642卷

作者： Liu, Chengcheng Su, Hang Cent Iron & Steel Res Inst Inst Struct Steel Beijing 100081 Peoples R China China Iron & Steel Res Inst Grp Mat Digital R&D Ctr Beijing 100081 Peoples R China

The composition of oxide glasses is characterized by high dimensionality and sparsity, making it challenging to establish high-precision predictive models. Therefore, feature extraction is essential. This study focuses on the optical properties of oxide glasses (refractive index and Abbe number), utilizing autoencoder (AE) and machine learning techniques to achieve automated feature extraction. The results indicate that compared to standalone neural networks (NN), AE-NN transforms unsupervised learning into supervised learning, reducing feature dimensions while improving model accuracy. Specifically, for the refractive index dataset, the dimensionality was reduced from 63 to 25, with a corresponding test set coefficient of determination (R2) of 0.95. For the Abbe number dataset, the dimensionality was reduced from 61 to 30, with a corresponding test set R2 of 0.97, demonstrating the effectiveness of the feature extraction method. Regarding interpretability, analyzing the encoder weight matrix of the AE-NN identified the importance of original features, with Co and Y being the most significant for both refractive index and Abbe number. Additionally, the application of the feature extraction method in machine learning models shows its generality in improving model performance, particularly for nonensemble models such as Support Vector Regression (SVR) or k-Nearest Neighbors (KNN), exhibiting significant accuracy enhancements. Finally, targeting lanthanide glasses, the established predictive model successfully identified novel optical glasses with high refractive index and Abbe number in the La2O3-Bi2O3 and La2O3-Ta2O5 systems, presenting new possibilities for optical component design.

关键词： Machine learning Oxide glass autoencoder Optical properties Transfer learning

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AttentivECGRU: GRU based autoencoder with attention mechanism and automated for ECG detection

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Roy, Moumita Halder, Anindya Majumder, Sukanta Biswas, Utpal Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 West Bengal India North Eastern Hill Univ Sch Technol Dept Comp Applicat Tura Campus Tura 794002 Meghalaya India

Electrocardiograms can reveal irregular cardiac cycles, i.e., arrhythmia and detecting arrhythmia from its morphology is challenging. This article proposes a novel approach for arrhythmia detection using Gated Recurrent Unit based autoencoder with an attention mechanism named AttentivECGRU, followed by fuzzy based threshold selection procedure. The proposed AttentivECGRU autoencoder has three segments namely AttentivECGRU-Encoder, AttentivECGRU-Latent-Attention Space Representation and AttentivECGRU-Decoder which are responsible respectively for generating latent space representation from the input signals, imposing relative score based on the importance of the features and reconstruct the input signals. The proposed method only requires the normal signals in the training phase;however, it can predict both normal and abnormal test signals effectively. Fuzzy based automated threshold selection procedure is also proposed to handle the overlapping nature of the reconstruction loss probability density function of normal and arrhythmia test signals. Results on ECG5000 dataset show that the proposed AttentivECGRU outperformed eleven other state-of-the-art methods, achieving the highest accuracy of 99.14% with Precision 0.9931, Recall 0.9682, and F1-score 0.9804. Confidence Interval tests confirm the statistical superiority of the proposed method over other compared techniques yielding the smallest error rate and error bound. Following the similar trend, the proposed method has produced very high predictive performance on TwoLeadECG dataset as well achieving 98.99% accuracy.

关键词： Electrocardiogram prediction autoencoder Gated Recurrent Unit Attention mechanism Fuzzy thresholding

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MPDA: Multivariate Probability Distribution autoencoder for Hyperspectral Anomaly Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Mu, Zhenhua Wang, Yihan Zhang, Yating Song, Chuanming Wang, Xianghai Liaoning Normal Univ Sch Geog Dalian 116029 Peoples R China Liaoning Normal Univ Sch Comp Sci & Artificial Intelligence Dalian 116029 Peoples R China Dalian Univ Sch Informat Engn Dalian 116622 Peoples R China

In recent years, the significant success of deep learning (DL) in computer vision has contributed to its continuous development in the field of hyperspectral image (HSI) anomaly detection (AD). However, in practical applications, HSI-AD based on DL faces many challenges due to the inability to effectively acquire training samples and predict the types of anomaly targets. This makes it a challenging task, especially for AD in complex scenes. In this article, we propose an unsupervised DL framework for HSI-AD based on the multivariate probability distribution autoencoder (MPDA). First, to explore the distribution characteristics of high-dimensional data, we use the probability density histogram to statistically distribute the frequencies of each interval adaptively, dividing the HSI and obtaining an AD-guided image through the designed grid structure. Second, we propose an unsupervised multilayer autoencoder network based on energy-weighted skip connections. By coupling the detection-guided image in the network, we achieve reverse-guided module reconstruction, weakening the feature representation of anomalous in the reconstructed information and enhancing the separability of targets. Finally, we model the reconstructed error images using the multivariate skewed t-distribution based on data distribution characteristics to obtain the final AD map. Through the comparative experiments with other innovative AD algorithms on authentic HSI datasets captured in five different scenarios, the proposed algorithm demonstrates strong generalization and detection capabilities. The source code of the MPDA will be public at https://***/muzhenhuam/MPDA/tree/master.

关键词： Image reconstruction Probability distribution Probability Feature extraction Detectors Partitioning algorithms Nonhomogeneous media Histograms Gaussian distribution Anomaly detection Anomaly detection (AD) autoencoder hyperspectral image (HSI) probability density distribution skewed distribution unsupervised deep learning (DL)

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Learnable faster kernel-PCA for nonlinear fault detection: Deep autoencoder-based realization

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2024年 40卷

作者： Ren, Zelin Jiang, Yuchen Yang, Xuebing Tang, Yongqiang Zhang, Wensheng Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Harbin Inst Technol Control & Simulat Ctr Harbin 150001 Peoples R China

Kernel principal component analysis (KPCA) is a well -recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel trick -based method, KPCA inherits two major problems. First, the form and the parameters of the kernel function are usually selected blindly, depending seriously on trial -and -error. As a result, there may be serious performance degradation in case of inappropriate selections. Second, at the online monitoring stage, KPCA has much computational burden and poor real-time performance, because the kernel method requires to leverage all the offline training data. In this work, to deal with the two drawbacks, a learnable faster realization of the conventional KPCA is proposed. The core idea is to parameterize all feasible kernel functions using the novel nonlinear DAE-FE (deep autoencoder based feature extraction) framework and propose DAE-PCA (deep autoencoder based principal component analysis) approach in detail. The proposed DAE-PCA method is proved to be equivalent to KPCA but has more advantage in terms of automatic searching of the most suitable nonlinear high -dimensional space according to the inputs, which helps to improve the accuracy of fault detection. Furthermore, the online computational efficiency improves by many times compared with the conventional KPCA. Finally, the Tennessee Eastman (TE) process benchmark and wastewater treatment plant (WWTP) benchmark are employed to illustrate the effectiveness of the proposed method, where the average fault detection rates of DAE-PCA are at least 0.27% and 4.69% higher than those of other methods, and its online computational efficiency is faster 90.48% and 24.57% times than that of KPCA respectively.

关键词： Kernel Principal Component Analysis (KPCA) Fault detection Process monitoring autoencoder Data-driven

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An Automatic Grading Model for Semantic Complexity of English Texts Using Bidirectional Attention-Based autoencoder

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第18期33卷

作者： Chen, Ruo Han Ng, Boon Sim Paramasivam, Shamala Ren, Li Univ Putra Malaysia Serdang 43400 Selangor Malaysia Univ Sains Malaysia Uam 11800 Penang Malaysia

With the advent of the information age, the massive increase of English text data puts forward higher requirements for text analysis and processing. The aim of this study is to accurately evaluate the semantic complexity of English text through an autoencoder structure based on bidirectional attention. This paper first analyzes the importance of automatic classification of semantic complexity in English text, and then builds an autoencoder structure based on bidirectional attention, which captures bidirectional information in text, and then uses the autoencoder structure for feature extraction and dimension reduction, which further strengthens the model's ability to capture semantic complexity. Finally, A Bidirectional Attention Self-Encoding English Text Semantic Complexity Automatic Grading Model (BSETG) is established. This study conducted experimental verification based on semantic Evaluation (SemEval) dataset, convolutional neural network (CNN)/Daily Mail dataset and Penn Treebank dataset, and conducted a comparative analysis with existing semantic complexity evaluation methods. The experimental results show that the overall accuracy of BSETG algorithm is maintained between 70% and 90%, the response speed of BSETG algorithm is relatively fast, and the success rate of BSETG algorithm is relatively stable to a large extent.

关键词： Semantic complexity automatic grading bidirectional attention autoencoder natural language processing

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