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The process of big data handling refers to the efficient management of storage and processing of a very large volume of data. The data in a structured and unstructured format require a specific approach for overall handling. The classifiers analyzed in this paper are correlative na{\"{i}}ve Bayes classifier (CNB), Cuckoo Grey wolf CNB (CGCNB), Fuzzy CNB (FCNB) and Holoentropy CNB (HCNB). These classifiers are based on the Bayesian principle and work accordingly. The CNB is developed by extending the standard na{\"{i}}ve Bayes classifier with applied correlation among the attributes to become a dependent hypothesis. The cuckoo search and grey wolf optimization algorithms are integrated with the CNB classifier and significant performance improvement is achieved. The resulting classifier is called a cuckoo grey wolf correlative na{\"{i}}ve Bayes classifier (CGCNB). Also, the performance of the FCNB and HCNB classifiers are analyzed with CNB and CGCNB by considering accuracy, sensitivity, specificity, memory and execution time.

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Journal of Big Data

Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework

The handling of big data refers efficient management of processing and storage requirements of very large volume of structured and an unstructured data of association. The basic approach for big data classification using naïve Bayes classifier is extended with correlation among the attributes so that it becomes a dependent hypothesis, and it is named as correlative naïve Bayes classifier (CNB). The optimization algorithms such as cuckoo search and grey wolf optimization are integrated with the correlative naïve Bayes classifier, and significant performance improvement is achieved. This model is called as cuckoo grey wolf correlative naïve Bayes classifier (CGCNB). The further performance improvements are achieved by incorporating fuzzy theory termed as fuzzy correlative naïve Bayes classifier (FCNB) and holoentropy theory termed as Holoentropy correlative naïve Bayes classifier (HCNB), respectively. FCNB and HCNB classifiers are comparatively analyzed with CNB and CGCNB and achieved noticeable performance by analyzing with accuracy, sensitivity and specificity analysis. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Design and Development of Bayesian Optimization Algorithms for Big Data Classification Based on MapReduce Framework

Big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data. One of the data mining techniques used widely and effectively to classify big data is the MapReduce model. In this paper, an approach for the classification of big data is developed using Cuckoo–Grey wolf based Correlative Naive Bayes classifier and MapReduce Model (CGCNB-MRM). Accordingly, a novel classifier, named Cuckoo–Grey wolf based Correlative Naive Bayes classifier (CG-CNB), is designed by modifying CNB classifier with a newly developed optimization algorithm, Cuckoo–Grey Wolf based Optimization (CGWO). CGWO algorithm is designed by the effective integration of Cuckoo Search (CS) Algorithm into Grey Wolf Optimizer (GWO), to optimize the CNB model by the optimal selection of the model parameters. Finally, the proposed CGCNB-MRM approach performs the classification for each data samples based on the probability index table and the posterior probability of the data. Three metrics, such as accuracy, sensitivity, and specificity, are utilized for the performance evaluation of the proposed CGCNB-MRM approach, where it could achieve 80.7% accuracy with 84.5% sensitivity and 76.9% specificity and thus, prove its effectiveness in big data classification.

Data & Knowledge Engineering

Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification

Control \& Cybernetics

A survey of big data classification strategies.

This paper surveys tools and techniques used in the Big-data and Big-data computing. The principles upon which Big-data and dataintensive computations work have been explored with some outlines provided by various researchers in the field. The paper starts with the traditionalcluster computing and further explores about the Hadoop system as a tool to solve Big-data issues. It also covers the significant growth in cloudcomputing towards hosting MapReduce to use for Data-intensive computations as one of the services available through clouds. It has brief coverageabout Microsoft Azure and Amazon clouds for MapReduce services to be provided through Internet.

International Journal of Scientific & Technology Research

Survey Of Technologies, Tools, Concepts And Issues In Big Data

Big data is the recent imminent technology, which can provide large benefits to the business administration. Owing to such huge volume, it becomes very complicated to ensure effective analysis by the existing techniques. The complications can be related to analyze, capture, sharing, storage, and visualization of the data. To tackle these challenges, a novel classification technique using Holoentropy based Correlative Naive Bayes classifier and MapReduce Model (HCNB-MRM) is proposed. The proposed HCNB, which is designed by combining the Holoentropy function with the correlative based Naive Bayes classifier deals with both high-dimensional data sets as well as extensive datasets to improve the benchmark, and classify the data based on dependent assumption. Therefore, the proposed HCNB-MRM is used to make the process simpler and to choose the best features from big dataset. The proposed HCNB with the MapReduce Model maximizes the performance of big data classification using probability index table, and posterior probability of the testing data samples. The performance of the proposed HCNB-MRM is evaluated using three metrics, such as accuracy, sensitivity, and specificity. From the experimental results, it is analyzed that the proposed HCNB-MRM obtains a high classification accuracy of 93.5965% and 94.3369% for the localization dataset, and skin dataset when compared with the existing techniques.

Evolutionary Intelligence

Holoentropy based Correlative Naive Bayes classifier and MapReduce model for classifying the big data

When owner stores his data on cloud that time for keeping his data confidential and to achieved access control, the owner can use encryption algorithm called attribute based encryption to encrypt his data. For achieving strongest form of access control, we are using to construct cipher text-policy attribute-based encryption in our work also we implemented Division and replication of Data in the Cloud for Optimal Performance and Security (DROPS) which secures data from threats and attacks in this DROP technique data is fragmented and stored using T-Coloring graph in cloud storage. Each node is able to store only one fragment of one file. Third Party Auditor will check integrity of stored data and inform the data owner

International Journal of Science & Engineering Development Research

CPABE and Data Integrity on Cloud Storage

In the cloud, to keep data confidential and to achieve access control, the data owners can use attribute-based encryption to encrypt data. At the time of encryption, the access policies are not flexible. To achieve the strongest form of access control, so for construction of circuit’s cipher text we are using circuit cipher text-policy attribute-based encryption in our work. We also propose Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) which provides solution for performance and security problems. In this methodology, file is separated into fragments and then encrypted and replicated fragments data stored on the nodes in cloud storage. Each node can stores only one fragment of data file that gives surety that even in case of a successful attack attacker will not get meaningful information. Third Party Auditor will check integrity of stored data and replace the tempered data with original one.

International Journal of Computer Engineering and Applications

CIRCUIT-CIPHERTEXT POLICY ATTRIBUTEBASED ENCRYPTION AND DATA INTEGRITY ON CLOUD STORAGE

The term “big data” means a large amount of data, and big data management refers to the efficient handling, organization, or use of large volumes of structured and unstructured data belonging to an organization. Due to the gradual availability of plenty of raw data, the knowledge extraction process from big data is a very difficult task for most of the classical data mining and machine learning tools. In a previous paper, the correlative naive Bayes (CNB) classifier was developed for big data classification. This work incorporates the fuzzy theory along with the CNB classifier to develop the fuzzy CNB (FCNB) classifier. The proposed FCNB classifier solves the big data classification problem by using the MapReduce framework and thus achieves improved classification results. Initially, the database is converted to the probabilistic index table, in which data and attributes are presented in rows and columns, respectively. Then, the membership degree of the unique symbols present in each attribute of data is found. Finally, the proposed FCNB classifier finds the class of data based on training information. The simulation of the proposed FCNB classifier uses the localization and skin segmentation datasets for the purpose of experimentation. The results of the proposed FCNB classifier are analyzed based on the metrics, such as sensitivity, specificity, and accuracy, and compared with the various existing works.

Journal of Intelligent Systems

FCNB: Fuzzy correlative naïve bayes classifier with MapReduce framework for big data classification

Abstract: Intrusion detection is a significant focus of research in the security of computer networks. This paper presents an analysis of the progress being made in the development of effective intrusion detection. Network security becomes more complex due to the changing environment of network and new type of attacks. So it is necessary to design dynamic system to detect new type of attacks. In this paper we define the solution to frequently changing network environment and new types of attacks. The designed system contains two models, Local model and Global model. In the local model, online Gaussian mixture models (GMMs) and online Adaboost processes are used as weak classifiers. A global detection model is constructed by combining the local parametric model. This combination is achieved by using an algorithm based on particle swarm optimization (PSO) and support vector machines (SVM). This system is able to detect new types of attacks. It gives high detection rate and low false alarm rate. Keywords: Adaboost; detection rate; false alarm rate; network intrusions; parameterized model.

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)

INTRUSION DETECTION IN DYNAMIC DISTRIBUTED NETWORK USING MACHINE LEARNING BASED ALGORITHMS

Due to the continuous arrival of plenty of raw data, the big data classification is getting much attention in various fields, such as industry, medicine, and financial business. Due to the urgent need of big data classification in those fields, several standard classifiers are adapted to perform the big data classification, but they are not able to deal with these huge data problems. More commonly, Navie Bayes classifier is mostly preferred for big data classification because of its simple computation procedure. However, the main drawback behind this classifier is the independence hypothesis, in which the input data are conditionally independent of each other. To overcome this drawback, we propose a new classification algorithm using Correlative Naïve Bayes classifier and MapReduce framework (CNB-MRF). Here, the newly proposed correlation function with MapReduce framework is used to make the classification based on the dependent hypothesis to improve the classification performance. Here, the data classification can be easily performed for every new sample through the probability index table of the training data sample and the posterior probability of the testing data samples using MapReduce framework. Then, the classification by the proposed CNB-MRF classifier is performed using localization and skin dataset. It is concluded that, the proposed CNB-MRF classifier achieves a high classification accuracy of 74.77% and 61.35% for localization and skin dataset respectively, as compared with Naïve bayes classifier and MapReduce framework (NB-MRF).