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ItemTowards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data( 2022-01-04)The vision of Industrie 4.0 includes the automated reduction of anomalies in flexibly combined production machine groups up to a zero-failure ideal. Algorithmic real-time detection of production anomalies may build the basis for machine self-diagnosis and self-repair during production. Several real-time anomaly detection algorithms appeared in recent years. However, different algorithms applied to the same data may result in contradictory detections. Thus, real-time anomaly detection in Industrie 4.0 machine groups may require a benchmark ranking for algorithms to increase detection results’ reliability. This paper makes a qualitative research contribution based on ten expert interviews to find design principles for such a benchmark ranking. The experts were interviewed on three categories, namely timeliness, thresholds and qualitative classification. The study’s results can be used as groundwork for a prototypical implementation of a benchmark.
ItemShadow IT Behavior of Financial Executives in Germany and Italy as an Antecedent to Internal Data Security Breaches( 2022-01-04)Data security breaches have been consistently identified in literature as significant, negative events. While most of the related research focuses on externally initiated breaches, far fewer studies provide clarity related to internally initiated breaches. The risk of internal breaches may be dramatically increased by shadow information technology (IT). Our study examines German and Italian financial executives’ decisions to engage in shadow IT in combination with two potential mitigation techniques (severity of sanctions in violation of IT policy and outcome effect related to breach risk). While Italian executives act as predicted, German executives engage in a different decision-making process whereby a self-service business culture brought on by perceived increased IT capabilities supersedes the level of cybersecurity awareness and a strong IT usage policy. Results also suggest an outcome effect favoring increased likelihood of breaches may lessen the likelihood of shadow IT usage. Our study adds an international component to existing data security breach and shadow IT research, while also contributing to the IT usage policy, neutralization theory, dynamic capabilities, outcome effect, and self-service literatures.
ItemHigh-Performance Fake Voice Detection on Automatic Speaker Verification Systems for the Prevention of Cyber Fraud with Convolutional Neural Networks( 2022-01-04)This study proposes a highly effective data analytics approach to prevent cyber fraud on automatic speaker verification systems by classifying histograms of genuine and spoofed voice recordings. Our deep learning-based lightweight architecture advances the application of fake voice detection on embedded systems. It sets a new benchmark with a balanced accuracy of 95.64% and an equal error rate of 4.43%, contributing to adopting artificial intelligence technologies in organizational systems and technologies. As fake voice-related fraud causes monetary damage and serious privacy concerns for various applications, our approach improves the security of such services, being of high practical relevance. Furthermore, the post-hoc analysis of our results reveals that our model confirms image texture analysis-related findings of prior studies and discovers further voice signal features (i.e., textural and contextual) that can advance future work in this field.