A Big Data Provenance Model for Data Security Supervision Based on PROV-DM Model

Gao, Yuanzhao; Du, Xuehui

doi:10.1109/access.2020.2975820

Cited by 31 publications

(10 citation statements)

References 30 publications

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“…However, there has been a great contradiction between the advanced nature of big data and the backwardness of some grass-roots tax officials. They have not fully utilized the big data [4].…”

Section: The Current Status and Causes Of Tax Risk Management In Chinamentioning

confidence: 99%

Tax Risk Prediction of Real Estate Based on Convolutional Neural Network

Yin¹,

Luo²

2021

Frontiers in Artificial Intelligence and Applications

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Risk management is an important link in tax administration. From China’s taxation practice, risk identification has become the weakness of tax management. With the complexity of massive data and the secrecy of modern transactions, traditional tax risk identification can no longer adapt to the development of the times. In the past, most risk researches focused on the basic machine learning stage. There are gaps in the application of deep learning in tax risk management. Based on the tax risk management indicators, this paper took the real estate industry as an example. We used convolutional neural network (CNN) to construct a tax risk prediction model. The experiment shows that a tax risk prediction model based on CNN has higher accuracy in tax risk identification and has a stronger ability to process tax data. The model has a certain reference value for tax authorities to reduce tax risk and tax loss.

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Section: The Current Status and Causes Of Tax Risk Management In Chinamentioning

confidence: 99%

Tax Risk Prediction of Real Estate Based on Convolutional Neural Network

Yin¹,

Luo²

2021

Frontiers in Artificial Intelligence and Applications

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“…This model makes a few crucial infrastructures with higher execution time. For data supervision, the big data provenance model (BDPM) was proposed by Gao et al [11]. Dissimilar components in the big data system with the entiredata transformation process are represented using diverse data processing modes and various data types.…”

Section: Related Workmentioning

confidence: 99%

“…The search space randomly determines the primary position of the solution. (11) The eligible seeds with their initial position are . Where the k th decision variable if j th candidate solution with its minimum and maximum values are and .…”

Section: Mathematical Representationmentioning

confidence: 99%

An Efficient Classification of Intrusion in Bigdata based on Chaos Game Optimization and Ensemble SVM

Ponmalar¹,

Dhanakoti²

2021

Preprint

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The growing popularity of the internet and network services has resulted in an increase in data in all fields. The data are increasing on the daily basis with high speed. This also creates some daunting issues such as security, storage, and so on. Meanwhile, the detection of intrusion from the big data in the ultra-high-speed environment is a critical task. Several intrusion detection methods are carried out to classify the big data based on intrusion and without intrusion. The optimum accuracy of big data classification, however, has yet to be achieved. Hence we proposed a novel ensemble SVM Model, in which the ensemble SVM is incorporated with the Chaos Game Optimization (CGO) algorithm, which can be exploited to enhance the classification accuracy. Our method also classifies the intrusion based on their types. It also classifies almost nine attacks as, Exploits, DoS, Backdoor, Generic, Worms, Analysis, Fuzzers, Shellcode, Reconnaissance. The experimental analysis is carried on the UNSW-NB15 big data dataset. The performance metrics precision, accuracy, recall, F-score are analyzed and compared with the state-of-art works such as BAMS-OIF, SAD, SMLsmBDA, and BDPM. The outcomes depict that the proposed work outperforms all the other existing works in terms of classification accuracy.

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“…In today's world, the situation has changed and a big amount of data can be fetched from enormous sources and in a variety of its formats. The various tools like hadoop distributed file system (HDFS) and MapReduce are used to store such a big amount of data [1], [2]. The role of big data in the NoSQL database is in an enormous form to achieve high performance and accuracy over conventional databases.…”

Section: Introductionmentioning

confidence: 99%

Security aware information classification in health care big data

Funde

Swain

2021

IJECE

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These days e-medical services frameworks are getting famous for taking care of patients from far-off spots, so a lot of medical services information like the patient’s name, area, contact number, states of being are gathered distantly to treat the patients. A lot of information gathered from the different assets is named big data. The enormous sensitive information about the patient contains delicate data like systolic BP, pulse, temperature, the current state of being, and contact number of patients that should be recognized and sorted appropriately to shield it from abuse. This article presents a weightbased similarity (WBS) strategy to characterize the enormous information of health care data into two classifications like sensitive information and normal information. In the proposed method, the training dataset is utilized to sort information and it comprises of three fundamental advances like information extraction, mapping of information with the assistance of the training dataset, evaluation of the weight of input data with the threshold value to classify the data. The proposed strategy produces better outcomes with various assessment boundaries like precision, recall, F1 score, and accuracy value 92% to categorize the big data. Weka tool is utilized for examination among WBS and different existing order procedures.

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A Big Data Provenance Model for Data Security Supervision Based on PROV-DM Model

Cited by 31 publications

References 30 publications

Tax Risk Prediction of Real Estate Based on Convolutional Neural Network

Tax Risk Prediction of Real Estate Based on Convolutional Neural Network

An Efficient Classification of Intrusion in Bigdata based on Chaos Game Optimization and Ensemble SVM

Security aware information classification in health care big data

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