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نتیجه جستجو - Security monitoring

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Automatic underground space security monitoring based on BIM
نظارت بر امنیت خودکار فضای زیرزمینی بر اساس BIM-2020
Traditional underground space safety monitoring is ineffective as data continuity is weak, systematic and random errors are prominent, data quantification is difficult, data stability is scarce (especially in bad weather), and it is difficult to guarantee human safety. In this study, BIM technology and multi-data wireless sensor network transmission protocol, cloud computing platform are introduced into engineering monitoring, real-time online monitoring equipment, cloud computing platform and other hardware and software are developed, and corresponding online monitoring system for structural safety is developed to realize online monitoring and early diagnosis of underground space safety. First, the shape of the underground space, the surrounding environment, and various monitoring points are modeled using BIM. Then, the monitoring data collected from sensors at the engineering site are transmitted to the cloud via wireless transmission. Data information management is then realized via cloud computing, and an actual state-change trend and security assessment is provided. Finally, 4D technology (i.e., 3D model + time axis) that leverages a deformation chromatographic nephogram is used to facilitate managers to view deformation and safety of their underground spaces. To overcome past shortcomings, this system supports the management of basic engineering project data and storage of historical data. Furthermore, the system continuously reflects the fine response of each monitoring item under various working conditions all day, which has significant theoretical value and application.
Keywords: BIM technology | Deformation monitoring | Automation information | Management model
مقاله انگلیسی
2 An efficient hybrid deep learning approach for internet security
یک رویکرد یادگیری عمیق ترکیبی کارآمد برای امنیت اینترنت-2019
Nowadays, internet is mostly used communication tool worldwide. However, the major problem of the internet is to provide security. To provide internet security, many researches and papers have been suggested about information and network security. The commonly used system against network attacks is firewalls. In this study, a novel firewall data classification approach is presented. This approach uses 10 cases to obtain numerical results. The proposed approach consists of data acquisition from Firewall, feature selection and classification steps. Firstly, the Firewall data were gathered from a Firewall. Then, the redundant features are eliminated and these features are normalized using min–max normalization. The obtained final feature sets are forwarded to classifiers. In the cases defined, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM) and Support Vector Machine (SVM) are utilized as classifiers. It was seen from the results, the deep learning approach are more successful than SVM classifier and the highest classification accuracy was calculated as 97.38% by using Bi-LSTM-LSTM hybrid network. The proposed method has several advantages and these are (1) the proposed method achieved high success rates using hybrid deep learning approaches (2) the training time of the proposed method is short (3) an intelligent network security monitoring method is presented using basic methods and deep learning. In addition, a useful approach has been presented to achieve high success rate at the end of the faster training process than traditional machine learning methods. Briefly, an intelligent monitoring system is proposed for network security.
Keywords: Deep learning | LSTM | Bi-LSTM | Network security | Classification | Big data
مقاله انگلیسی
3 Simultaneous learning of reduced prototypes and local metric for image set classification
یادگیری همزمان از نمونه های اولیه کاهش یافته و متریک محلی برای طبقه بندی مجموعه تصویر-2019
Classification based on image set is recently a competitive technique, where each set contains multiple images of a person or an object. As a widely used model, affine hull has shown its power in modeling image set. However, due to the existence of noise and outliers, the over-large affine hull usually matches fails when two hulls overlapped. Aiming at alleviating this handicap, this paper proposes a novel method for image set classification, namely Learning of Reduced Prototypes and Local Metric (LRPLM). Specifi- cally, for each gallery image set, a reduced set of prototypes and an optimal local feature-wise metric are simultaneously learned, which jointly minimize the loss function involved the estimation of classifi- cation error probability. In doing so, LRPLM inherits the merits of affine hull with better representation to account for the unseen appearances and makes use of the powerful discriminative ability improved by the local metric. It looks like that LRPLM pulls similar image sets with the same class label “closer”to each other, while pushing dissimilar ones “far away”. Extensive experiments illustrate the considerable effectiveness of LRPLM on three widely used datasets. As we know, classification is a research hotspot in expert and intelligent systems. Different from the previous classification methods, LRPLM focuses on im- age set-based classification technology, while most of them are single-shot classification technology. Thus, the proposed method can be considered as an expert system technology for medical diagnosis, security monitoring, object categorization, and biometrics recognition applications.
Keywords: Image set classification | Prototype learning | Metric learning | Face recognition | Expert system
مقاله انگلیسی
4 A structured methodology for deploying log management in WANs
یک روش ساخت یافته برای استقرار مدیریت ورود به سیستم در شبکه های WAN-2017
Article history:Available online 6 March 2017Keywords:Log management Security monitoring SIEMSocial network analysisThe collection of log data is a challenging operation for organizations that wish to monitor their infras- tructure for security reasons. In this paper a methodology for the implementation of a log management infrastructure for real-time security monitoring on a large scale infrastructure is proposed. Related meth- ods are adjusted and adopted to compose parts of the proposed methodology, avoiding to “re-invent the wheel” where possible. Social network analysis is employed to make and justify decisions that were for- merly performed either intuitively or based on experience and vendors’ best practices. The methodology concludes with the creation of a repository of the necessary data. The result is an innovative methodol- ogy that can be used as a step-by-step guide for the implementation of a log management infrastructure in an organization. The proposed methodology is applied to a real WAN.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Log management | Security monitoring | SIEM | Social network analysis
مقاله انگلیسی
5 Cross-heterogeneous-database age estimation through correlation representation learning
ارزیابی سن پایگاه متقارن ناهمگن از طریق یادگیری نمایش مجدد همبستگی-2017
Human age estimation is an important research topic and has found its applications in such scenarios as commodity recommendation and security monitoring. The design of existing estimators generally fol lows a same pipeline, i.e., an estimator is built from a given training dataset and then evaluated on a holdout testing set from the same dataset to display its effectiveness. In doing so, an implicit assumption is that both training and testing sets should share the same age distribution and feature representation, consequently-meaning that (1) once the age of a face image to be tested is outside the age range of training set, a mis-estimation is naturally inevitable; (2) an estimator built on a specific dataset usually cannot be directly applied to make evaluations on other datasets, because the dimensions and types of their feature representations are usually different (i.e., these datasets are heterogeneous). That is, exist ing methods can not be directly employed to perform cross-heterogeneous-dataset age estimation. To the best of our knowledge, the age distributions of existing aging datasets are usually not consistent but complementary to each other. Motivated by such a complementarity characteristic of different datasets in age distributions, we develop a so-called correlation component manifold space learning (CCMSL) to first learn a common feature space by capturing the correlations between the heterogeneous databases, and then in the resulting space establish a single age estimator across such heterogeneous datasets through correlation representation learning (CRL). As a result, not only can the age-distribution-incompleteness of individual aging datasets be compensated, but also the discriminating ability of the estimator be rein forced. Finally, experimental results demonstrate the superiority of the proposed methods.
Keywords: Age estimation | Cross-heterogeneous-database | Correlation component manifold space | learning | Correlation representation learning
مقاله انگلیسی
6 GPU-based parallel optimization of immune convolutional neural network and embedded system
بهینه سازی موازی بر اساس GPU از شبکه عصبی کانولوشن ایمنی و سیستم جاسازی شده-2017
Up to now, the image recognition system has been utilized more and more widely in the security monitoring, the industrial intelligent monitoring, the unmanned vehicle, and even the space exploration. In designing the image recognition system, the traditional convolutional neural network has some de- fects such as long training time, easy over-fitting and high misclassification rate. In order to overcome these defects, we firstly used the immune mechanism to improve the convolutional neural network and put forward a novel immune convolutional neural network algorithm, after we analyzed the network structure and parameters of the convolutional neural network. Our algorithm not only integrated the location data of the network nodes and the adjustable parameters, but also dynamically adjusted the smoothing factor of the basis function. In addition, we utilized the NVIDIA GPU (Graphics Processing Unit) to accelerate the new immune convolutional neural network (ICNN) in parallel computing and built a real-time embedded image recognition system for this ICNN. The immune convolutional neural net- work algorithm was improved with CUDA programming and was tested with the sample data in the GPU-based environment. The GPU-based implementation of the novel immune convolutional neural network algorithm was made with the cuDNN, which was designed by NVIDIA for GPU-based accel- erating of DNNs in machine learning. Experimental results show that our new immune convolutional neural network has higher recognition rate, more stable performance and faster computing speed than the traditional convolutional neural network.& 2016 Elsevier Ltd. All rights reserved.
Keywords:Immune algorithm | Convolutionalneuralnetwork | Image recognition | Parallelcomputing | Embedded system | Security monitoring
مقاله انگلیسی
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