Attribute space design for behavioral anomaly detection in CRM systems

Abstract

Attribute space design for behavioral anomaly detection in CRM systems

Khalizev K.A.

Incoming article date: 10.04.2025

The article proposes a methodology for design an attribute space to detect behavioral anomalies of users in CRM systems. It describes methods for recording actions through integrated trackers that capture user activity, clicks, cursor movements, and keystrokes. The aggregation of this data into feature vectors enables the application of machine learning algorithms to detect anomalies and enhance information security in the CRM system.

Keywords: information security, CRM system, behavioral analysis, anomaly detection, user identification, behavioral analytics, activity monitoring, digital footprint, insider threats, attribute space