ERP security

Enterprise resource planning (ERP) systems are often viewed as a necessary burden that usually delivers less value than originally anticipated. Enterprise resource planning systems are supposed to provide organizations with the information they need for central control, regulatory compliance, and business processes that are supported by information flowing through the system. Big Data and AI models present an opportunity to redefine how ERP is implemented and maintained. It has the potential to move the ERP space from being mundane to being a significant driver of future business performance. AI, by automating human performance, can also automate system ownership and improve customer satisfaction. In this way, it is a force multiplier that changes the economics of the ERP ecosystem. The data being fed into the model and the results of that model present new challenges for ensuring that all of this can be done at the proper level of security. This is a central challenge for ensuring the long-term security of both the ERP implementation and the enterprise itself. This paper will examine the protocols that must be observed to retain security and maximize this potential transformation associated with the AI-driven future of ERP solutions. Both the opportunities and the potential pitfalls will be discussed in the hopes that this will enable a secure and efficient path to that future, minimizing the number of disruptions that a company will have to encounter on that path.

The article outlines the development of neural network frameworks to counter future cyberattacks in Enterprise Resource Planning (ERP) systems. A lack of security can lead to major risks. Hence, an ERP system is more prone to cyberattacks. Many researchers have suggested the integration of big data analytics to counter cyberattacks. We have designed a neural network framework for binary classification to predict the attack classes in real-time. We have compared various types of neural networks to check which neural network is more effective and has less error in predicting cyberattacks. The findings revealed that out of the proposed methodologies, the ensemble of the Recurrent Neural Network as an autoencoder is the most effective design. We proposed three designs, and we have found that the ensemble of deep learning provides a 97.5% error rate. This design is most effective for big data architecture in the real-time pipeline of ERP. The trends of deep learning work on big data but face some practical issues in implementing the models. The study is beneficial for organizations using ERP, which is the largest ERP vendor and the most costly product widely used worldwide. Hence, it provides research in the field of cybersecurity and contributes to the latest technology approach redesign for practitioners. Deep learning methodologies face practical challenges in the real-time representation of any event. The relevance of the computing approach of the novel and deep learning model in practice is identified, and it is performed by the researchers.