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Title: :  Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage
PaperId: :  26766
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 7 Issue 1 2021
DUI:    16.0415/IJARIIE-26766
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Karthik KushalaCeler Systems Inc, Folsom, California, USA
R. PushpakumarVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu

Abstract

Information Technology
Edge Computing, Cloud Storage, Anomaly Detection, Autoencoder-GAN, Healthcare Data.
Anomaly detection is a key function in protecting and managing data in sensitive environments such as healthcare, finance, and cloud storage networks. This paper presents an Edge-to-Cloud synergy model that leverages the strengths of both edge and cloud computing to attain efficient and scalable anomaly detection. The architecture uses a shallow autoencoder at the edge level for initial anomaly filtering and bandwidth optimization to ensure data privacy and responsiveness. Only suspicious marked information or summary data is transmitted to the cloud and not everything, in order to save on transmission and reduce latency. Within the cloud layer, an advanced Autoencoder-GAN framework is applied to perform deep anomaly detection. The autoencoder captures normal behavioral patterns and tags anomalies with high reconstruction error, while the GAN improves detection by learning subtle data distributions and generating adversarial samples to further improve robustness. The combined framework ensures accurate detection, especially for high-dimensional, heterogeneous, and imbalanced data. The method begins with data collection from three key sources: anonymized EHRs for healthcare, SEC EDGAR filings for economic data, and AWS CloudTrail logs on Kaggle for cloud transactions. Pre-processing includes cleaning, normalization, encoding, and partitioning into training, validation, and test sets. Everything in the pipeline was implemented in Python using TensorFlow, Keras, and Scikit-learn libraries. Experimental testing confirms that the proposed model is superior to traditional methods, with high precision, recall, and accuracy. The last model obtained a 95.8% accuracy in anomaly detection, confirming the performance of the Edge-to-Cloud Autoencoder-GAN framework.

Citations

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IJARIIE Karthik Kushala, and R. Pushpakumar. "Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage" International Journal Of Advance Research And Innovative Ideas In Education Volume 7 Issue 1 2021 Page 1958-1967
MLA Karthik Kushala, and R. Pushpakumar. "Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage." International Journal Of Advance Research And Innovative Ideas In Education 7.1(2021) : 1958-1967.
APA Karthik Kushala, & R. Pushpakumar. (2021). Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage. International Journal Of Advance Research And Innovative Ideas In Education, 7(1), 1958-1967.
Chicago Karthik Kushala, and R. Pushpakumar. "Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage." International Journal Of Advance Research And Innovative Ideas In Education 7, no. 1 (2021) : 1958-1967.
Oxford Karthik Kushala, and R. Pushpakumar. 'Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage', International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 1, 2021, p. 1958-1967. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Edge_to_Cloud_Synergy__An_Autoencoder_GAN_Framework_for_Anomaly_Detection_in_Healthcare_Records__Financial_Statements__and_Secure_Cloud_Storage_ijariie26766.pdf (Accessed : ).
Harvard Karthik Kushala, and R. Pushpakumar. (2021) 'Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage', International Journal Of Advance Research And Innovative Ideas In Education, 7(1), pp. 1958-1967IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Edge_to_Cloud_Synergy__An_Autoencoder_GAN_Framework_for_Anomaly_Detection_in_Healthcare_Records__Financial_Statements__and_Secure_Cloud_Storage_ijariie26766.pdf (Accessed : )
IEEE Karthik Kushala, and R. Pushpakumar, "Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage," International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 1, pp. 1958-1967, Jan-Feb 2021. [Online]. Available: https://ijariie.com/AdminUploadPdf/Edge_to_Cloud_Synergy__An_Autoencoder_GAN_Framework_for_Anomaly_Detection_in_Healthcare_Records__Financial_Statements__and_Secure_Cloud_Storage_ijariie26766.pdf [Accessed : ].
Turabian Karthik Kushala, and R. Pushpakumar. "Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 7 number 1 ().
Vancouver Karthik Kushala, and R. Pushpakumar. Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud Storage. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2021 [Cited : ]; 7(1) : 1958-1967. Available from: https://ijariie.com/AdminUploadPdf/Edge_to_Cloud_Synergy__An_Autoencoder_GAN_Framework_for_Anomaly_Detection_in_Healthcare_Records__Financial_Statements__and_Secure_Cloud_Storage_ijariie26766.pdf
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