A Software Package for Detecting Anomalies in User Authentication

Abstract

Anomaly detection is a very important tool for various applications such as intrusion detection, fraud, malfunction, system health monitoring and event detection in IoT devices. Recently, user authentication has become an extremely popular topic in information security research environments. The definition of user authentication is formulated as the process of verifying the identity declared by the user for a system object. Authentication is a method used to distinguish between true or false authentication requests. There are many methods used to authenticate a user that can identify valid users in protected resources. This article discusses various methods for analyzing abnormal user behavior in information systems, namely such methods as machine learning, neural networks, hybrid methods. Based on the analysis of system logs in the Astra Linux operating system, a software package has been developed to identify anomalies when trying to authenticate users.