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Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier for Secured Big Data Communication
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 32 |
| Year of Publication: 2023 |
| Authors: S.L. Swapna, V. Saravanan |
10.5120/ijca2023923078
|
S.L. Swapna, V. Saravanan . Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier for Secured Big Data Communication. International Journal of Computer Applications. 185, 32 ( Aug 2023), 1-8. DOI=10.5120/ijca2023923078
Abstract
The application of big data analytics and related technologies like the Internet of Things (IoT) facilitates user intentions and behaviours as well as operational decision-making. Security is the major concern in the application of big data analytics to protect the system and secure the information as well as the data being handled. Conventional security techniques have become inefficient in terms of processing and identifying network threats in a reasonable amount of time. To deal with this problem, a unique Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier based Secured Data Communication (CSDECGBAC-SDC) model with improved accuracy and lower time complexity is introduced. The CSDECGBAC-SDC model's core functions for enhancing security include user registration, data collection, and data communication. During the registration process, the user's information is initially registered. Following that, the CSDECGBAC-SDC model collects data from the enrolled user. The Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier is used in the CSDECGBAC-SDC Model to accomplish user authentication for anyone who want to access the data. For detecting the authorized user, the ensemble technique uses a group of weak learners as a Tversky Indexive Chi-square automatic interaction detection decision tree. The weak learner results are combined. Finally, cardinal voting is applied to find the majority vote in data classification by using the gradient ascent function. This in turn helps to improve secured data communication. Experimental evaluation is carried out on factors such as classification accuracy, error rate, and classification time with respect to a number of users. The results indicate that the CSDECGBAC-SDC model effectively improves the classification accuracy with minimum error rate and classification time than the conventional approaches.
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