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.

Enterprise databases are the heart of applications and contain the most sensitive and critical information of organizations. While there have been significant advances in the security of databases, vulnerabilities still exist due to mistakes made by application developers, database administrators, and users. Manual detection and patching of such vulnerabilities typically take months, but an automated detection and remediation framework is proposed to fill the gap and eliminate a significant number of these vulnerabilities in near-real time. This framework comprises two key components: a detection engine that leverages static analysis to identify potential patches, coupled with query dynamic testing and fuzzing to identify exploitable misconfigurations; and an orchestration engine that applies detected patches on the database, validates the accuracy of the fix, and rolls back changes if the problem is not resolved. A prototype of this framework has been implemented and validated on a real-time database deployed in an enterprise environment. Because of the complexity of the problem landscape, the research focus is on static vulnerability detection and automated corrective actions. These two capabilities can greatly reduce the manual workload associated with vulnerability detection and significantly enhance the assurance that the granted privileges validate the least privilege principle. The proposed architecture aims to enable the deployment of a detection-and-remediation solution that minimizes human effort, reduces the enterprise-at-risk window, and maximizes the volume of detected vulnerabilities.