| Title | An anomaly detection approach for enterprise file integration |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Üzüm, İbrahim, Can, Özgü |
| Conference Name | 2018 6th International Symposium on Digital Forensic and Security (ISDFS) |
| Date Published | mar |
| Keywords | accountability, alarm system, anomaly detection, composability, corporate systems, data integrity, effective self-learning anomaly detection module, emergency management, emergency services, enterprise file integration, enterprise systems, file integration, file malfunction, file size, file structure, file transfer processes, file transfers, Information security, information system, integration channels, integration duration, Intrusion detection, learning (artificial intelligence), machine learning, Metrics, middleware, network accountability, novel anomaly detection approach, organization work process management, Organizations, pubcrawl, real-time file integrations, Resiliency, security issues, security of data, Standards organizations, transfer logs, transferring files |
| Abstract | An information system based on real-time file integrations has an important role in today's organizations' work process management. By connecting to the network, file flow and integration between corporate systems have gained a great significance. In addition, network and security issues have emerged depending on the file structure and transfer processes. Thus, there has become a need for an effective and self-learning anomaly detection module for file transfer processes in order to provide the persistence of integration channels, accountability of transfer logs and data integrity. This paper proposes a novel anomaly detection approach that focuses on file size and integration duration of file transfers between enterprise systems. For this purpose, size and time anomalies on transferring files will be detected by a machine learning-based structure. Later, an alarm system is going to be developed in order to inform the authenticated individuals about the anomalies. |
| DOI | 10.1109/ISDFS.2018.8355376 |
| Citation Key | uzum_anomaly_2018 |
An anomaly detection approach for enterprise file integration