@Article{crph1238,
AUTHOR = {Maka, Srinivasa Rao and Bodepudi, Varun and Routhu, KishanKumar and Jha, Krishna Madhav and Chinta, Purna Chandra Rao and Sakuru, Manikanth},
TITLE = {A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {139-155},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1238},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2020.1238},
ABSTRACT = {Deep learning approaches are very useful to enhance cybersecurity protocols for industry-integrated big data enterprise resource planning systems. This research study develops deep learning architectures of variational autoencoder, sparse autoencoder, and deep belief network for detecting anomalies, fraud, and preventing cybersecurity attacks. These cybersecurity issues occur in finance, human resources, supply chain, and marketing in the big data integrated ERP systems or cloud-based ERP systems. The main objectives of this creative research work are to identify the vulnerabilities in various ERP systems, databases, and the interconnected domains; to introduce a conceptual cybersecurity network model that incorporates variational autoencoders, sparse autoencoders, and deep belief networks; to evaluate the performance of the proposed cybersecurity model by employing the appropriate parameters with real-time and synthetic databases and simulated scenarios; and to validate the model performance by comparing it with traditional algorithms. A big data platform with an integrated business management system is known as an integrated ERP system, which plays an instrumental role in conducting business for various organizations in society. In recent times, as uncertainty and disparity increase, the cyber ecosystem becomes more complex, volatile, dynamic, and unpredictable. In particular, the number of cyber-attacks is increasing at an alarming rate; the resultant security breaches have a disruptive and disturbing effect on businesses around the world, with a loss of billions of dollars. To combat these threats, it is essential to develop a conceptual cybersecurity network model to secure systems by functioning as a mutually supporting and strengthening network model rather than working in isolation. In this dynamic and fluid environment, introducing a deep learning approach helps to support and prevent fraud and other illicit activities related to human resources and the supply chain, among others. Some cybersecurity vulnerabilities include, for example, database vulnerabilities, service level vulnerabilities, and system vulnerabilities, among others. The proposed methodology focuses only on database vulnerabilities, with the main aim of detecting and mitigating new potential vulnerabilities in other dependent domains as a future initiative.},
}

%0 Journal Article
%A Maka, Srinivasa Rao
%A Bodepudi, Varun
%A Routhu, KishanKumar
%A Jha, Krishna Madhav
%A Chinta, Purna Chandra Rao
%A Sakuru, Manikanth
%D 2021
%J Current Research in Public Health

%@ 2831-5162
%V 1
%N 1
%P 139-155

%T A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems
%M doi:10.31586/jaibd.2020.1238
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1238

TY  - JOUR
AU  - Maka, Srinivasa Rao
AU  - Bodepudi, Varun
AU  - Routhu, KishanKumar
AU  - Jha, Krishna Madhav
AU  - Chinta, Purna Chandra Rao
AU  - Sakuru, Manikanth
TI  - A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems
T2  - Current Research in Public Health
PY  - 2021
VL  - 1
IS  - 1
SN  - 2831-5162
SP  - 139
EP  - 155
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1238
AB  - Deep learning approaches are very useful to enhance cybersecurity protocols for industry-integrated big data enterprise resource planning systems. This research study develops deep learning architectures of variational autoencoder, sparse autoencoder, and deep belief network for detecting anomalies, fraud, and preventing cybersecurity attacks. These cybersecurity issues occur in finance, human resources, supply chain, and marketing in the big data integrated ERP systems or cloud-based ERP systems. The main objectives of this creative research work are to identify the vulnerabilities in various ERP systems, databases, and the interconnected domains; to introduce a conceptual cybersecurity network model that incorporates variational autoencoders, sparse autoencoders, and deep belief networks; to evaluate the performance of the proposed cybersecurity model by employing the appropriate parameters with real-time and synthetic databases and simulated scenarios; and to validate the model performance by comparing it with traditional algorithms. A big data platform with an integrated business management system is known as an integrated ERP system, which plays an instrumental role in conducting business for various organizations in society. In recent times, as uncertainty and disparity increase, the cyber ecosystem becomes more complex, volatile, dynamic, and unpredictable. In particular, the number of cyber-attacks is increasing at an alarming rate; the resultant security breaches have a disruptive and disturbing effect on businesses around the world, with a loss of billions of dollars. To combat these threats, it is essential to develop a conceptual cybersecurity network model to secure systems by functioning as a mutually supporting and strengthening network model rather than working in isolation. In this dynamic and fluid environment, introducing a deep learning approach helps to support and prevent fraud and other illicit activities related to human resources and the supply chain, among others. Some cybersecurity vulnerabilities include, for example, database vulnerabilities, service level vulnerabilities, and system vulnerabilities, among others. The proposed methodology focuses only on database vulnerabilities, with the main aim of detecting and mitigating new potential vulnerabilities in other dependent domains as a future initiative.
DO  - A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems
TI  - 10.31586/jaibd.2020.1238
ER  -