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共 3354 条记录，以下是481-490 订阅

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Dynamic-Enabled Defense Strategy Base on Improved CVSS for the Home Internet 4th

Dynamic-Enabled Defense Strategy Base on Improved CVSS for t...

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4th international conference on Cloud computing and Security (ICCCS)

作者： Zhou, Chunru Lei, Min Li, Kunchang Xu, Li Bi, Wei China Acad Engn Phys Inst Comp Applicat Mianyang 621900 Sichuan Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr Beijing 100876 Peoples R China Guizhou Univ Guizhou Prov Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China SeeleTech Corp San Francisco CA 94107 USA Zsbatech Corp Beijing 100088 Peoples R China

ISBN: (纸本)9783030000189;9783030000172

Nowadays, home Internet has attracted a lot of interest. Dynamic-enabled Defense Strategy can reduce the possibility of being hacked to become the mining systems for blockchain. In order to reflect the status of the smart home system more accurately, this paper proposed a method to improve the common vulnerability scoring system (CVSS) and make CVSS more suitable for the smart home system. The proposed method can obtain vulnerabilities scores, reflecting the security status of the system accurately and providing strong support for system security defense and reinforcement. For dynamically-enabled defense with constantly changing state, it is hard for the attacker to obtain all the information in a short time, since it requires too many resources when the changing frequency is too high. Whereas the attacker has enough time to analyze the system with low changing frequency. Combining with the improved vulnerability scoring system, we propose a dynamic-enable defense strategy based on Markov chain and stochastic Petri net to calculate the attack detection probability and the vulnerability value of the system, and obtain the best dynamic switching interval to ensure the security of the system.

关键词： Smart home Internet of things Security evaluation Dynamical defense Blockchain

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Impact of CDR data Analysis Using Big data Technologies for the Public: An Analysis 4

Impact of CDR Data Analysis Using Big Data Technologies for ...

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4th international conference on advanced computing and Communication Systems (ICACCS)

作者： Nair, Suja C. Elayidom, M. Sudeep Gopalan, Sasi Cochin Univ Sci & Technol Cochin Kerala India Cochin Univ Sci & Technol Sch Engn Cochin Kerala India

ISBN: (纸本)9781509045594

A Call data Record (CDR) is an information structure storing applicable information about a given telephonic activity including a customer of a telephonic framework. A CDR, as a rule, contains spatial and temporal data and it can convey other extra helpful information. The primary motivation of this survey is to investigate the current procedures in call data records so that the specialists can incorporate all the fundamental measurements in their works in this domain and the limitations of the current ones can be overcome. In this survey, assorted issues incorporated into call data record strategies is recognized and distinctive call data record systems are studied to find which attributes is tended to in a given and which is overlooked. To categorize the strategies, all articles that had "call data records" in its title or as its keyword published between 2004 to 2016, is initially chosen from the scientific journals: IEEE, Elsevier, Springer and international journals. Furthermore, this study gives a detailed idea regarding call data record.

关键词： Call data Record telephonic activity spatial tempora.l

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Sludge Volume Index (SVI) Modelling: data mining Approach 38th

Sludge Volume Index (SVI) Modelling: Data Mining Approach

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38th international conference on Information Systems Architecture and Technology (ISAT)

作者： Szelag, Bartosz Gawdzik, Jaroslaw Studzinski, Jan Kielce Univ Technol PL-25314 Kielce Poland Polish Acad Sci PL-01447 Warsaw Poland

ISBN: (纸本)9783319672236;9783319672229

In this paper, statistical models to forecast based on the sludge volume index (SVI) with the continuous measurements carried out in the period from 2013 to 2016 for waste water treatment Sitkowka-Nowiny was developed at the same, for two variants of analyses. In the first one, a model of SVI predicting based on the quality indicators of wastewater flowing into the treatment plant, i.e. Biochemical (BOD) and chemical oxygen demand (COD), the content of total nitrogen (TN) and ammonia nitrogen (NH4), total suspended solids, total phosphorus (TP) and the operating parameters of the bioreactor (pH, temperature, oxygen concentration in the nitrification chamber). In the second case, the possibility of replacing individual measurements of the quality of wastewater values calculated on the basis of daily sewage flows to the treatment plant was examined. The above mentioned models statistical analysis was performed using the method of k-nearest neighbor (k-NN), cascading neural network (CNN) and boosted tree (BT). To evaluate the predictive ability of these models the average relative error (MAE) and absolute error (MAPE) were used. The conducted analysis showed that based on the above mentioned indicators of effluent quality and technological parameters of the biological reactor it is possible to modeling of sediment volume index with satisfactory accuracy. In the case under consideration methods of lower values of the prediction error of SVI obtained using a cascade neural networks (MAE = 17.49 ml/g and MAPE = 9.80%) than for the method k-nearest neighbor (MAE = 27.85 ml/g and MAPE = 14.50%). Furthermore, based on the performed simulation, it was found that it is possible to model the analyzed work of the quality of waste water on the basis of the daily flow with reasonable accuracy, it is confirmed by the calculated value of the average and absolute and relative error, and the better ability predictive characterized by the models obtained on the basis CNN than k-NN. In

关键词： Sludge volume index Cascade neural network K-nearest neighbor method Wastewater treatment

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Big data mining Algorithms for Fog computing 2017

Big Data Mining Algorithms for Fog Computing

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international conference on Big data and Internet of Things (BDIOT)

作者： Fong, Simon Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Taipa Macau Peoples R China

ISBN: (纸本)9781450354301

Fog computing is a contemporary distributed computing concept extending from Cloud computing, which pushes the data analytics to the edge of a sensor network as far as possible. It helps avoid performance bottleneck and data analytics latency at the central server of a Cloud. However, when Fog computing is deployed, the edge nodes are responsible in data analysis including learning and recognizing patterns from the incoming data streams. Hence it is crucial to find appropriate data mining algorithm(s) which is lightweight in operation and accurate in predictive performance. In this paper, the suitability of data mining and data stream mining algorithms are investigated in Fog computing environment. Specifically, non-black-box machine learning models such as decision trees are looked into, with a quick pre-processing function implemented by correlation-based feature selection algorithm coupled with traditional search methods and particle swarm optimization search method. The simulation is based on an IoT environment where emergency services are to be supported. The results of this paper sheds light into what/which algorithms should be designed and chosen for delivering edge intelligence under Fog computing environment.

关键词： Computer network security security threats SME

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Preprocessing and Feature Selection Approach for Efficient Sentiment Analysis on Product Reviews 5th

Preprocessing and Feature Selection Approach for Efficient S...

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5th international conference on Frontiers in Intelligent computing - Theory and Applications (FICTA)

作者： Ghosh, Monalisa Sanyal, Gautam Natl Inst Technol Dept Comp Sci & Engn Durgapur W Bengal India

ISBN: (纸本)9789811031533;9789811031526

In the recent years opinion mining plays an important role by business analyst before launching a product. Opinion mining mainly concerns about detecting and extracting the feature from various opinion rich resources like review sites, discussion forum, blogs and news corpora so on. The data obtained from those are highly unstructured in nature and very large in volume, therefore data preprocessing plays an essential role in sentiment analysis. Researchers are trying to develop newer algorithm. This research paper attempts to develop a better opinion mining algorithm and the performance has been worked out.

关键词： Information retrieval Web data analysis Preprocessing Opinion mining Feature selection N-gram model

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Efficient Privacy Preserving Integrity Checking Model for Cloud data Storage Security 8

Efficient Privacy Preserving Integrity Checking Model for Cl...

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8th international conference on advanced computing (ICoAC)

作者： Subha, T. Jayashri, S. Sri Sai Ram Engn Coll Kovur India Anna Univ Madras Tamil Nadu India Adhiparasakthi Engn Coll Melmaruvathur India

ISBN: (纸本)9781509058884

Cloud computing is a growing technology that offers compute, storage and network resources as a service over the internet. It enables the individuals, clients or the enterprises to outsource their data and application software to the cloud server. The services are offered by a cloud service provider (CSP) and the users need to pay for what they use. There are many security concerns needs to be addressed when the data is maintained by third party service provider in cloud. The auditor is introduced to audit the integrity of the data on behalf of the client in order to ensure integrity of data. This can be called as public auditability of data. Recently, two privacy preserving auditing mechanisms named Oruta and Knox are introduced to check the correctness of stored data. In this paper, we try to propose the security flaw of their scheme when active adversaries are involved in cloud storage. An active adversary is capable of modifying the data stored in cloud arbitrarily. This data modification is not being identified by the user and the auditor in the verification process. We try to suggest a solution to resolve this flaw by signing the proof response generated on the cloud server side. Then the signed proof is sent to the trusted third party auditor (TTPA) for verification. The auditor first verifies the signature and for the validation of the proof. The proposed scheme is proved to be secure against active adversary.

关键词： Auditing cloud storage data integrity privacy preserving TTPA Security

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An Effective data Storage Model for Cloud databases using Temporal data De-duplication Approach 8

An Effective Data Storage Model for Cloud Databases using Te...

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8th international conference on advanced computing (ICoAC)

作者： Muthurajkumar, S. Vijayalakshmi, M. Kannan, A. Anna Univ Coll Engn Guindy Dept Informat Sci & Technol Chennai 600025 Tamil Nadu India

ISBN: (纸本)9781509058884

The aim of this paper is to design a system that has benefits equally for the client (cloud storage user) and the cloud storage provider. For the benefit of user, an improved security model for the data stored on the cloud is provided by encrypting the data even before uploading the file to the storage server. On the other side, the capacity of the cloud storage is optimally utilized by avoiding redundant storage of the same file on the server. Encrypting the same file by using two different algorithms produces two different encrypted files which in turn affects the performance by the de-duplication concept of the service provider. So an efficient mechanism which generates the hash value of the text and uses this hash value towards encrypting the data is proposed which uses hash values with unique file identifier. While the client uploads file to the cloud database, the unique id of the file is compared with those stored into the server. The proposed algorithm called Temporal data De-duplication Algorithm (TDDdA) is used to get better performance of data storage in cloud. Since our approach is based on time, the read and write response time of the cloud data is linearly reduced. Increased security is achievable through our proposed model.

关键词： data De-duplication data Storage Temporal data Cloud database Cloud computing

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A novel data quality assessment framework for vehicular network testbeds 12

A novel data quality assessment framework for vehicular netw...

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12th EAI international conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017

作者： Tian, Daxin Zhu, Yukai Zhou, Jianshan Duan, Xuting Wang, Yunpeng Song, Jeungeun Rong, Hui Guo, Peng Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing XueYuan Road No.37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No.68 Tianjin300300 China Beihang University Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering XueYuan Road No.37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781631901577

Big data technique is considered as a powerful tool to exploit all the potential of the Internet of Things and the smart cities. The development of internet of Vehicles (IoV) and wireless communication technologies have boosted diverse applications related to smart cities and Cyber-Physical Systems, but the data quality of vehicular sensors is an important issue due to the high-speed mobile wireless communication environment and physical sensor noise. This paper presents our experiences for big data analytics based on a vehicular network testbed, in terms of sensors data management, multi-dimension data fusion and data quality assessment for the vehicular sensor data. The proposed data quality assessment framework consist of feature extraction based on multi-sensor data fusion and multi-level wavelet transform, as well as a semi-supervised learning based classification algorithm. The comparison experiment shows that the proposed framework and approaches can extract feasible features and solve the unbalanced label problems, which achieve a better assessment effect. Copyright © 2018 EAI.

关键词： Smart city

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Improving Influenza Forecasting with Web-Based Social data

Improving Influenza Forecasting with Web-Based Social Data

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international conference on Advances in Social Network Analysis and mining, ASONAM

作者： Carmela Comito Agostino Forestiero Clara Pizzuti National Research Council of Italy (CNR) Institute for High Performance Computing and Networking (ICAR) Rende CS Italy

ISBN: (纸本)9781538660522

Improving seasonal influenza forecasting combining official data sources with web search and social media is a recent research topic which can enhance situational awareness of healthcare organizations when monitoring the outbreak of seasonal flu. In this paper, a prediction model based on autoregression that combines data coming from official influenza surveillance system, with data from web search and social media regarding influenza is proposed. The model is evaluated on the two influenza seasons 2016-2017 and 2017-2018, restricted to Italy. The results show that by using Web-based social data, like Google search queries and tweets, we can obtain accurate weekly influenza predictions up to four weeks in advance. The proposed approach improves real-time influenza forecast compared to traditional surveillance systems based on data from sentinel doctors: the prediction error is reduced up to 47%, while the Pearson's correlation is improved of about 24%.

关键词： Google Twitter Predictive models Forecasting Market research data models Surveillance

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Application of Semantic computing in Cancer on Secondary data Analysis

Application of Semantic Computing in Cancer on Secondary Dat...

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IEEE international conference on Robotic computing (IRC)

作者： Charles C.N. Wang I-Seng Chang Phillip C.Y. Sheu Jeffrey J. P. Tsai Department of Biomedical Informatics Asia University Wufeng Taichung Taiwan Department of Electrical Engineering and Computer Science University of California Irvine CA

There have been an enormous number of publications on cancer research. These integrated but unstructured cancer-related articles are of great value for cancer diagnosis, treatment and prevention. In this study, we use the basic concepts underlying text mining and semantic computing to discuss the current state-of-the-art text mining applications in cancer research. Using the abstract of literature extracted from PubMed between 2008 and 2016, a total 925,648 articles are used for subsequent text mining. Among the 925,648 articles, the top 5 most studied cancer types were breast cancer (23.82%), lung cancer (10.54%), prostate cancer (9.90%), rectal cancer (8.44%), and ovarian cancer (4.44%). The top 3 most frequently occurred keywords in the abstracts of the 925,648 articles are patients, cancer, and cell where each appear 1,445,688, 1,284,140, and 676,924 times, respectively. Analysis of the key concepts indicate that the most common concepts are patients, cancer, cell and tumor. Our results suggest that while the risk factors of cancer, treatment of cancer, and survival of cancer patients were popular research topics, end-of-life cancer care issues are less studied. Further studies should explore these areas since they are as important as treatment of the disease itself for many patients.

关键词： Text mining Tag clouds Tumors Semantics Breast cancer

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