The cybersecurity industry has been locked in a battle against quickly evolving cyber threats for some time. By leveraging artificial intelligence (AI) and machine learning (ML), User and Entity Behavior Analytics (UEBA) seeks to offer a next-generation solution for threat detection. UEBA can identify abnormal behaviors that are often missed by conventional systems, providing deeper visibility into user activities and potential threats.
This article demystifies UEBA, explaining how it supports modern security strategies, enhances detection of insider threats, reduces false positives, and provides a more contextual method for detecting threats and improving cybersecurity posture.
User and Entity Behavior Analytics (UEBA) uses machine learning and advanced analytics to detect abnormal behaviors by users and entities (like devices or applications) within a network. UEBA focuses on monitoring actions, such as login patterns or data access, to identify potential threats, such as insider attacks or compromised accounts. By first using machine learning to establish a baseline of normal behavior UEBA systems are then used to analyze real-time activities to identify statistical deviations from this baseline.
UEBA and other ML based detection solutions are attractive solutions due to the sheer complexity and dynamic nature of IT operations. Creating complex rules that can detect all possible indicators of malicious activity is nearly impossible. By leveraging statistical analysis, UEBA avoids the need to configure so many rules. This ML-based approach can more intelligently catch sophisticated threats that go unnoticed by rule-based detection systems such as YARA rules.
UEBA (User and Entity Behavior Analytics) solutions can be installed on both endpoints and network devices, depending on an organization’s security needs. Here's a breakdown of where and why UEBA is used:
Endpoints: Installing UEBA on endpoints (such as workstations, laptops, and servers) allows the system to monitor individual user activities. This includes tracking login patterns, file access, application usage, and other endpoint behaviors.
Network Devices: UEBA can also be installed on network devices like routers, firewalls, or switches to monitor traffic patterns, data flow, and device interactions across the entire network. This provides a broader perspective, allowing UEBA to detect anomalies in network traffic, indications of compromise (IoC), lateral movement across the network, privilege escalation, or unusual access to resources from unexpected devices or at unexpected times.
Hybrid Approach: A hybrid approach, (where UEBA is deployed both on endpoints and network devices) allows for comprehensive monitoring of user behavior across both individual devices and the network as a whole, improving the system's ability to detect and correlate anomalies that might otherwise go unnoticed.
UEBA uses advanced machine learning algorithms and statistical analysis, for anomaly detection. This helps identify threats that traditional approaches miss, and also reduces false positives. UEBA systems start by establishing a behavioral baseline for each user and entity, such as typical login times, device usage, and data access patterns, by aggregating logs and historical activity data. This baseline is represented mathematically using statistical distributions like Gaussian distributions to model normal behavior, where each data feature (e.g., username, role, login times, source IP address, time of day, geolocation, etc.) is assigned a mean (μ) and standard deviation (σ).
Once a baseline is established, UEBA continuously monitors and analyzes activity in real time and uses distance metrics such as Mahalanobis Distance to calculate how far each new data point deviates from the established mean. If a data point falls outside a certain threshold—often defined by a specific number of standard deviations (e.g., >3σ)—it is flagged as anomalous and alerts are sent to SOC team members for further investigation along with the event's metadata. By combining these and other mathematical techniques, UEBA can detect subtle, hidden threats like insider attacks, privilege escalation, or lateral movement, which traditional rule-based systems might miss.
Like every cybersecurity tool, UEBA has inherent strengths and limitations, which adversaries often exploit. There are three key elements that businesses need to address when using UEBA to safeguard their digital assets effectively:
Data Quality: Even the most advanced machine learning algorithms used in UEBA are ineffective without high-quality data. Poor or incomplete data can lead to false positives or missed detections, limiting UEBA’s ability to catch malicious behavior accurately.
Integration with Other Tools: Communication between systems is essential to provide a complete picture of security events across the organization. UEBA solutions must seamlessly integrate with other security platforms, particularly Security Information and Event Management (SIEM) systems. Without this integration, the overall effectiveness of UEBA in identifying and responding to threats is reduced.
Fast Pace of Change: Cybersecurity is a constantly evolving field, and as hackers develop more innovative tactics, security systems must be flexible enough to integrate new tools and technologies. UEBA systems that do not support this kind of adaptability may become outdated quickly, unable to address emerging threats.
User and Entity Behavior Analytics (UEBA) offers a robust solution for detecting sophisticated threats by leveraging machine learning to monitor deviations from normal user and entity behavior patterns. It excels at identifying insider threats, account compromises, and lateral movement, among other risks. Implementing UEBA involves risk assessment, system training, and continuous monitoring to ensure precise detection and response. Organizations benefit from UEBA’s ability to detect complex threats that traditional security tools might miss.
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