Login Analysis System for Enterprise Network Security
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Solution Overview
Problem
Existing security measures in enterprise networks face challenges in analyzing authentication log data due to a semantic gap between recorded and actual authentication activities, noisy domain controller logs, and the introduction of new users and machines, making it difficult to identify suspicious user logins.
Innovation Solution
A method is developed to process log data from enterprise networks, generate user and host profiles based on historical login information, create a graph of login activities, and identify inconsistent login instances by analyzing these profiles and graphs to detect suspicious user logins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain controller logs are analyzed to detect suspicious logins, then authentication activity can be monitored, but the logs become extremely noisy with multiple authentication messages and automated processes
Solution Approach 1:
The patent segments the authentication log data into distinct categories (e.g., interactive logins, automated process logins, service logins) and creates separate profiles for each type. This segmentation allows the system to filter and analyze only relevant login types, reducing noise from automated processes while maintaining detection accuracy for suspicious user logins.
Solution Approach 2:
The patent extracts and isolates specific authentication events from the noisy log data by filtering for particular login types and removing automated process entries. This extraction creates a cleaned dataset that focuses on user authentication behavior, making suspicious activity detection more reliable without the distraction of routine automated operations.
2Reliability
If user login patterns are monitored to detect anomalies, then suspicious activity can be identified, but new users and machines continuously introduced into the network create false anomalies
Solution Approach 1:
The patent performs preliminary actions by creating baseline profiles for users and machines before analyzing login behavior. These profiles capture normal authentication patterns and device relationships in advance, establishing what constitutes typical behavior. When new users or machines are introduced, their initial logins are compared against these pre-established profiles, allowing the system to distinguish between legitimate new connections and actual anomalies.
Solution Approach 2:
The patent implements dynamic profile updates that adapt to changing network conditions. When new users or machines are detected, the system updates their profiles with observed behavior patterns, allowing the detection mechanism to learn and accommodate legitimate changes in the network environment. This dynamic adaptation prevents false anomalies from being flagged as suspicious activity.
3Reliability
If comprehensive login analysis is performed to identify suspicious activity, then security can be enhanced, but the semantic gap between recorded and actual authentication activity remains
Solution Approach 1:
The patent introduces intermediary profile objects that serve as mediators between the raw log data and the security analysis. These profiles capture contextual information about users, machines, and their relationships, filling in the semantic gaps between what is recorded in logs and what actually represents authentication behavior. By analyzing logins through the lens of these intermediary profiles, the system can accurately distinguish between legitimate and suspicious activity despite the limitations of raw log data.
Data Source
AI summary
Methods, apparatus and articles of manufacture for identifying suspicious user logins in enterprise networks are provided herein. A method includes processing log data derived from one or more data sources associated with an enterprise network, wherein the enterprise network comprises multiple hosts; generating a set of profiles, wherein the set comprises a profile corresponding to each of multiple users and a profile corresponding to each of the multiple hosts, wherein each profile comprises one or more login patterns based on historical login information derived from said log data; and analyzing a login instance within the enterprise network against the set of profiles.