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Ethical Issues of User Behavioral Analysis through Machine Learning



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Journal of Information Systems Security
Volume 13, Number 1 (2017)
Pages 317
ISSN 1551-0123 (Print)
ISSN 1551-0808 (Online)
Georg Thomas — Charles Sturt University, Australia
Patrick Duessel — University of Bonn, Germany
Michael Meier — University of Bonn, Germany
Information Institute Publishing, Washington DC, USA




Due to the ever-growing risk of data leakage and sabotage by internal employees, insider threat detection is receiving increasing attention. Solutions are typically asset-centric and rule-based, providing limited detection capabilities and significant maintenance efforts. Content-based anomaly detection over user behavior is an alternative, but raises ethical questions that need to be addressed before deployment. In this contribution, user-centric content-based behavioral anomaly detection utilizing four ethical dimensions reveals that it requires integration with the organization's data privacy organization, a binding code of conduct for administrative personnel, integration with the organization's security incident management and continuous oversight by management.





Workplace Moral Conduct, Data Leakage Risk Mitigation, Professional Integrity, A nomaly Detection, Content Features, Security, Insider Threat, Privilege Misuse




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