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1.
Comput Intell Neurosci ; 2021: 1759111, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34471405

RESUMO

As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless, due to a lack of distinctiveness in the hyperspectral pixels of separate classes, there is a recurrent inseparability obstacle in the primary space. Additionally, an open challenge stems from examining efficient techniques that can speedily classify and interpret the spectral-spatial data bands within a more precise computational time. Hence, in this work, we propose a 3D-2D convolutional neural network and transfer learning model where the early layers of the model exploit 3D convolutions to modeling spectral-spatial information. On top of it are 2D convolutional layers to handle semantic abstraction mainly. Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. The experimental result shows that our model can efficiently improve the hyperspectral imagery classification, including an instantaneous representation of the spectral-spatial information. Our model evaluation on five publicly available hyperspectral datasets, Indian Pines (IP), Pavia University Scene (PU), Salinas Scene (SA), Botswana (BS), and Kennedy Space Center (KSC), was performed with a high classification accuracy of 99.85%, 99.98%, 100%, 99.82%, and 99.71%, respectively. Quantitative results demonstrated that it outperformed several state-of-the-arts (SOTA), deep neural network-based approaches, and standard classifiers. Thus, it has provided more insight into hyperspectral image classification.


Assuntos
Aprendizado de Máquina , Redes Neurais de Computação , Humanos , Análise de Componente Principal
2.
Sensors (Basel) ; 21(17)2021 Aug 24.
Artigo em Inglês | MEDLINE | ID: mdl-34502593

RESUMO

Data analysis plays an increasingly valuable role in sports. The better the data that is analysed, the more concise training methods that can be chosen. Several solutions already exist for this purpose in the tennis industry; however, none of them combine data generation with a wristband and classification with a deep convolutional neural network (CNN). In this article, we demonstrate the development of a reliable shot detection trigger and a deep neural network that classifies tennis shots into three and five shot types. We generate a dataset for the training of neural networks with the help of a sensor wristband, which recorded 11 signals, including an inertial measurement unit (IMU). The final dataset included 5682 labelled shots of 16 players of age 13-70 years, predominantly at an amateur level. Two state-of-the-art architectures for time series classification (TSC) are compared, namely a fully convolutional network (FCN) and a residual network (ResNet). Recent advances in the field of machine learning, like the Mish activation function and the Ranger optimizer, are utilized. Training with the rather inhomogeneous dataset led to an F1 score of 96% in classification of the main shots and 94% for the expansion. Consequently, the study yielded a solid base for more complex tennis analysis tools, such as the indication of success rates per shot type.


Assuntos
Esportes , Tênis , Análise de Dados , Aprendizado de Máquina , Redes Neurais de Computação
3.
Sensors (Basel) ; 21(17)2021 Aug 24.
Artigo em Inglês | MEDLINE | ID: mdl-34502594

RESUMO

Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms.


Assuntos
Pólipos do Colo , Neoplasias Colorretais , Inteligência Artificial , Pólipos do Colo/diagnóstico , Colonoscopia , Neoplasias Colorretais/diagnóstico , Humanos , Aprendizado de Máquina , Redes Neurais de Computação
4.
Sensors (Basel) ; 21(17)2021 Aug 25.
Artigo em Inglês | MEDLINE | ID: mdl-34502601

RESUMO

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm's monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects' left and right wrists.


Assuntos
COVID-19 , Dispositivos Eletrônicos Vestíveis , Algoritmos , Inteligência Artificial , Hospitais , Humanos , Aprendizado de Máquina , Monitorização Fisiológica , SARS-CoV-2 , Processamento de Sinais Assistido por Computador , Punho
5.
Sensors (Basel) ; 21(17)2021 Aug 25.
Artigo em Inglês | MEDLINE | ID: mdl-34502619

RESUMO

Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas concentration. Considering there exist very few instances of high gas concentration, the instance distribution of gas concentration would be extremely imbalanced. Therefore, such regression models generally perform poorly in predicting high gas concentration instances. In this study, we consider early warning of gas concentration as a binary-class problem, and divide gas concentration data into warning class and non-warning class according to the concentration threshold. We proposed the probability density machine (PDM) algorithm with excellent adaptability to imbalanced data distribution. In this study, we use the original gas concentration data collected from several monitoring points in a coal mine in Datong city, Shanxi Province, China, to train the PDM model and to compare the model with several class imbalance learning algorithms. The results show that the PDM algorithm is superior to the traditional and state-of-the-art class imbalance learning algorithms, and can produce more accurate early warning results for gas explosion.


Assuntos
Algoritmos , Aprendizado de Máquina , China , Carvão Mineral , Probabilidade
6.
Sensors (Basel) ; 21(17)2021 Aug 26.
Artigo em Inglês | MEDLINE | ID: mdl-34502630

RESUMO

In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.


Assuntos
Indústrias , Aprendizado de Máquina , Inquéritos e Questionários
7.
Sensors (Basel) ; 21(17)2021 Aug 26.
Artigo em Inglês | MEDLINE | ID: mdl-34502646

RESUMO

The electro-mechanical impedance (EMI) technique has been applied successfully to detect minor damage in engineering structures including reinforced concrete (RC). However, in the presence of temperature variations, it can cause false alarms in structural health monitoring (SHM) applications. This paper has developed an innovative approach that integrates the EMI methodology with multilevel hierarchical machine learning techniques and the use of fiber Bragg grating (FBG) temperature and strain sensors to evaluate the mechanical performance of RC beams strengthened with near surface mounted (NSM)-fiber reinforced polymer (FRP) under sustained load and varied temperatures. This problem is a real challenge since the bond behavior at the concrete-FRP interface plays a key role in the performance of this type of structure, and additionally, its failure occurs in a brittle and sudden way. The method was validated in a specimen tested over a period of 1.5 years under different conditions of sustained load and temperature. The analysis of the experimental results in an especially complex problem with the proposed approach demonstrated its effectiveness as an SHM method in a combined EMI-FBG framework.


Assuntos
Engenharia , Polímeros , Impedância Elétrica , Aprendizado de Máquina , Temperatura
8.
Sensors (Basel) ; 21(17)2021 Aug 27.
Artigo em Inglês | MEDLINE | ID: mdl-34502667

RESUMO

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.


Assuntos
Algoritmos , Tecnologia sem Fio , Teorema de Bayes , Análise por Conglomerados , Aprendizado de Máquina
9.
Sensors (Basel) ; 21(17)2021 Aug 27.
Artigo em Inglês | MEDLINE | ID: mdl-34502670

RESUMO

Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.


Assuntos
Algoritmos , Aprendizado de Máquina não Supervisionado , Fractais , Aprendizado de Máquina , Aprendizado de Máquina Supervisionado
10.
Sensors (Basel) ; 21(17)2021 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-34502692

RESUMO

Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.


Assuntos
Poluição do Ar , Algoritmos , Humanos , Aprendizado de Máquina , Projetos de Pesquisa
11.
Sensors (Basel) ; 21(17)2021 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-34502693

RESUMO

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of overfitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm-the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) with the policy optimization and ensemble learning. This algorithm presents an optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assess the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.


Assuntos
Algoritmos , Redes Neurais de Computação , Aprendizado de Máquina , Políticas
12.
Sensors (Basel) ; 21(17)2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34502718

RESUMO

In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel.


Assuntos
Rubus , Aprendizado de Máquina , Microscopia Eletrônica de Varredura , Polissacarídeos , Pós , Espectroscopia de Infravermelho com Transformada de Fourier
13.
Sensors (Basel) ; 21(17)2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34502725

RESUMO

In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people's diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples' dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.


Assuntos
Solanum tuberosum , Agricultura , Nariz Eletrônico , Humanos , Aprendizado de Máquina
14.
Sensors (Basel) ; 21(17)2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34502743

RESUMO

Document imaging/scanning approaches are essential techniques for digitalizing documents in various real-world contexts, e.g.,  libraries, office communication, managementof workflows, and electronic archiving [...].


Assuntos
Aprendizado de Máquina , Fluxo de Trabalho
15.
Sensors (Basel) ; 21(17)2021 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-34502757

RESUMO

Among the reasons for traffic accidents, distractions are the most common. Although there are many traffic signs on the road that contribute to safety, variable message signs (VMSs) require special attention, which is transformed into distraction. ADAS (advanced driver assistance system) devices are advanced systems that perceive the environment and provide assistance to the driver for his comfort or safety. This project aims to develop a prototype of a VMS (variable message sign) reading system using machine learning techniques, which are still not used, especially in this aspect. The assistant consists of two parts: a first one that recognizes the signal on the street and another one that extracts its text and transforms it into speech. For the first one, a set of images were labeled in PASCAL VOC format by manual annotations, scraping and data augmentation. With this dataset, the VMS recognition model was trained, a RetinaNet based off of ResNet50 pretrained on the dataset COCO. Firstly, in the reading process, the images were preprocessed and binarized to achieve the best possible quality. Finally, the extraction was done by the Tesseract OCR model in its 4.0 version, and the speech was done by the cloud service of IBM Watson Text to Speech.


Assuntos
Condução de Veículo , Acidentes de Trânsito , Aprendizado de Máquina , Leitura
16.
Sensors (Basel) ; 21(17)2021 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-34502765

RESUMO

Grain moisture content (GMC) is a key indicator of the appropriate harvest period of rice. Conventional testing is time-consuming and laborious, thus not to be implemented over vast areas and to enable the estimation of future changes for revealing optimal harvesting. Images of single panicles were shot with smartphones and corrected using a spectral-geometric correction board. In total, 86 panicle samples were obtained each time and then dried at 80 °C for 7 days to acquire the wet-basis GMC. In total, 517 valid samples were obtained, in which 80% was randomly used for training and 20% was used for testing to construct the image-based GMC assessment model. In total, 17 GMC surveys from a total of 201 samples were also performed from an area of 1 m2 representing on-site GMC, which enabled a multi-day GMC prediction. Eight color indices were selected using principal component analysis for building four machine learning models, including random forest, multilayer perceptron, support vector regression (SVR), and multivariate linear regression. The SVR model with a MAE of 1.23% was the most suitable for GMC of less than 40%. This study provides a real-time and cost-effective non-destructive GMC measurement using smartphones that enables on-farm prediction of harvest dates and facilitates the harvesting scheduling of agricultural machinery.


Assuntos
Algoritmos , Smartphone , Grão Comestível , Aprendizado de Máquina , Redes Neurais de Computação
17.
Sensors (Basel) ; 21(17)2021 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-34502773

RESUMO

People with severe disabilities require assistance to perform their routine activities; a Human-Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user's learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.


Assuntos
Robótica , Algoritmos , Eletroculografia , Movimentos Oculares , Humanos , Aprendizado de Máquina
18.
Sensors (Basel) ; 21(17)2021 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-34502786

RESUMO

Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.


Assuntos
Aprendizado de Máquina , Redes Neurais de Computação , Algoritmos , Máquina de Vetores de Suporte
19.
Sensors (Basel) ; 21(17)2021 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-34502815

RESUMO

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.


Assuntos
Esquizofrenia , Mídias Sociais , Humanos , Aprendizado de Máquina , Esquizofrenia/diagnóstico
20.
Sensors (Basel) ; 21(17)2021 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-34502833

RESUMO

Information and communication technologies have essential impacts on people's life. The real time convenience of the internet greatly facilitates the information transmission and knowledge exchange of users. However, network intruders utilize some communication holes to complete malicious attacks. Some traditional machine learning (ML) methods based on business features and deep learning (DL) methods extracting features automatically are used to identify these malicious behaviors. However, these approaches tend to use only one type of data source, which can result in the loss of some features that can not be mined in the data. In order to address this problem and to improve the precision of malicious behavior detection, this paper proposed a one-dimensional (1D) convolution-based fusion model of packet capture files and business feature data for malicious network behavior detection. Fusion models improve the malicious behavior detection results compared with single ones in some available network traffic and Internet of things (IOT) datasets. The experiments also indicate that early data fusion, feature fusion and decision fusion are all effective in the model. Moreover, this paper also discusses the adaptability of one-dimensional convolution and two-dimensional (2D) convolution to network traffic data.


Assuntos
Internet das Coisas , Comunicação , Humanos , Aprendizado de Máquina
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