Identification of atypical behavior of bank employees when using e-mail to prevent information leakages

Abstract

The article presents the results of using machine learning methods to identify atypical behavior of bank employees when using e-mail. A feature space is formed that characterizes the behavior of e-mail users. The objects were previously clustered using the density-based spatial clustering of applications with noise (DBSCAN) and the fuzzy logic elements. The objects were marked using the inbuilt business rules, and the training sample was formed in the absence of marked data. The most informative features are selected, and a model of classification of e-mail users by the type of their behavior is constructed. A feature space is formed that defines the characteristics of a particular message to identify messages that are the information security incidents. Preliminary data processing was performed by removing the duplicates and encoding the categorical variables. A model of messages classification is constructed. The best combination of the machine learning method and the feature selection algorithm was determined using quality metrics. The constructed models allow specialists of cybersecurity departments of banks to identify employees with abnormal behavior and possibly involved in information leaks. A software tool in Python was developed that makes it easier to identify the final status of a message by partially replacing its manual detection for an automatic one.