Occupational fraud significantly impacts company finances and reputation. Enterprise Resource Planning (ERP) systems store extensive data that can be used for fraud detection, though legitimate transactions dominate the records, making fraud detection a challenging task. Companies should share findings on evolving fraud patterns to stay current and combat fraud collaboratively. However, data security and privacy concerns hinder the sharing of relevant ERP data and information on fraud incidents. Federated Learning (FL) offers a promising solution by enabling the exchange of model parameters instead of sensitive data. This study evaluates the effectiveness of FL for fraud detection in ERP systems compared to traditional machine learning methods. It considers scenarios involving companies not contributing fraud data, varying proportions of fraud cases, different fraud categories, and data volumes, and their influence on the FL process. Federated training, centralized training, and isolated training are compared, highlighting the potential of FL for enhancing occupational fraud detection in ERP systems.

Enterprise Resource Planning (ERP) systems are used by companies to support and automate business processes. Users need to be granted the necessary permissions to be able to perform their work. Following the principle of least privilege, these permissions shall restrict the access to such information and resources only, which are required to complete the tasks involved. However, even using a well-attuned authorization concept, some users may still misuse the ERP system to enrich themselves. Besides a reduction in profit, companies suffer a loss of reputation and trust from their stakeholders. Furthermore, they may be faced with lawsuits from aggrieved customers or suppliers. Such occupational fraud shall therefore be traced and tracked down. Since all business operations are recorded within an ERP system, a variety of different data sources is available. This paper explores the wealth of data sources found in SAP ERP, the most widespread ERP system from SAP, the world’s leading vendor of ERP systems. It examines, in how far such data sources are suited for fraud detection. Previous literature is surveyed. It turns out that the applicability of the data used in previous work is limited or that no suitable data sources are available for developing and evaluating fraud detection techniques. Therefore, this paper proposes three data sets extracted from SAP ERP. The data sets are made available via GitHub and machine learning techniques are applied to evaluate the adequacy for fraud detection. In addition, the feature importance is examined to increase the transparency of fraud detection.

Enterprise Resource Planning (ERP) systems are used to support and to control the business processes of a company or organization. Such systems integrate the data across the entire company into a complete system that is capable of enhancing the key operations in virtually any department. Commonly, running an ERP system helps companies operate more efficiently, however, this also leads to problems. Employees are able to enrich themselves through insider knowledge or by exploiting incomplete or incorrect permission settings. This is referred to as occupational fraud. Since ERP systems keep records of all executed business activities and log system events and permission checks, they provide a variety of different data sources that can be used to detect occupational fraud. This paper reviews existing literature in the area of fraud detection and fraud cases based on ERP data including business data and system data. Using the most widespread ERP system from SAP, the potential and the suitability of the various data sources with respect to fraud detection is examined.

The solution space of an optimization problem consists of all its feasible solutions. In this work, we present a systematic literature review on the application of Information Visualization (IV) techniques for understanding and exploring such solution spaces. The review was conducted on several search databases, and we identified 264 papers that satisfied our inclusion criteria. A performance filter was applied to these papers, and we further analyzed and extracted data from 65 of them. Our analysis shows that there are a variety of solution space visualization approaches and provides useful references to support further studies on the subject.

Role Based Access Control is a widely spread concept in cyber security. Thus, the (NP-complete) Role Mining Problem (RMP), which consists of finding an optimal set of roles and a corresponding assignment of those roles to users, is of great scientific interest. Over the last years, different algorithms have been developed to search for good solutions to the RMP. However, conclusive benchmarks for thorough comparison of the developed methods are rarely known. This paper introduces to RMPlib, a library for the Role Mining Problem, containing a set of new industry-oriented benchmark instances partly taken from real-world use cases, partly created synthetically. Access to RMPlib is provided through a platform where researchers can actively contribute new benchmark instances and best solutions, such that the library adapts to the changing requirements in science. The current version of RMPlib can be found at https://github.com/RMPlib/RMPlib.

This paper proposes a design of a database system which accelerates the execution of database transactions by offloading database operators (e.g. joins, scans and sorting) in hardware algorithms executed on runtime reconfigurable computing platforms. Furthermore, a hybrid database system is described which exploits the strengths of the new reconfigurable hardware-based database system in combination with pre-existing technologies for main memory or disc resident database systems. Due to the parallel and pipelined design style of the hardware algorithms, the new database system offers a potential speedup over the traditional sequential execution on instruction stream processors. Moreover, data access times can be reduced by placing circuits on the reconfigurable fabric close to embedded memory which further allows for customizing high bandwidth memory interfaces. As a consequence of the accelerated execution speed, the new system has the potential to reliably meet constraints in real-time scenarios, to reduce the chance of lock contention and cache flushes, and to decrease the cost for concurrency control.

We propose to modify a type of ant algorithm called Population based Ant Colony Optimization (P-ACO) to allow implementation on an FPGA architecture. Ant algorithms are adapted from the natural behavior of ants and used to find good solutions to combinatorial optimization problems. General layout on the FPGA and algorithmic description are covered The most notable achievements featured in this paper are a runtime reduction and including the approximation of the heuristic function by a small set of favored decisions which changes over time.