Since the landscape of cybersecurity keeps evolving, identifying threats within an organization is a critical yet challenging task. Traditional methods of detecting anomalies, such as sifting through alerts, connecting dots, and actively hunting threats, can be time-consuming and labour-intensive.
Moreover, advanced threats like zero-day attacks and advanced persistent threats (APTs) can easily evade detection by conventional security tools, making their discovery and mitigation all the more crucial.
This is where User and Entity Behavior Analytics (UEBA) comes into play, revolutionizing the approach to threat detection by using advanced analytics and machine learning. In this article, we will delve into what it is, its components, functions, and use cases.
What is UEBA?
User and Entity Behavior Analytics is a category of security solutions that employ innovative analytics technologies, including machine learning and deep learning, to identify abnormal and risky behaviours exhibited by users, machines, and other entities within a corporate network.
Unlike traditional security tools that rely on predefined rules or patterns, User and Entity Behavior Analytics analyzes behaviours to uncover anomalies that indicate potential security incidents, thus providing a more proactive and comprehensive defence mechanism.
UEBA does not just monitor human behaviour but extends its surveillance to machines. For instance, if a server suddenly starts handling an unusually high number of requests, this could signify a potential Distributed Denial-of-Service (DDoS) attack. Such anomalies, which might go unnoticed by human administrators, are quickly identified by User and Entity Behavior Analytics systems. It triggers appropriate responses to mitigate the threat.
Brief History of User and Entity Behavious Analytics
Now that you understand what this system is, let’s explore its history. The term “UEBA” (User and Entity Behavior Analytics) was introduced in 2015 by Gartner. They expanded the existing UBA (User Behavior Analytics) category to include “entity” to highlight the importance of monitoring not just user behaviour but also other components of a network like routers, servers, applications, endpoints, and devices.
Since its inception, User and Entity Behavior Analytics has become crucial in cybersecurity, especially with the rise in insider threats and advanced attacks that traditional security tools often miss. Predictions suggest that the UEBA market will grow significantly, potentially reaching $4.2 billion by 2026.
Today, User and Entity Behavior Analytics is commonly integrated into various security platforms instead of being a standalone solution. As cyber threats continue to evolve, UEBA and similar technologies will also advance to better protect modern organizations.
Components of UEBA
A strong User and Entity Behavior Analytics solution comprises three major components: data analytics, data integration, and data presentation.
1. Data Analytics
Data analytics forms the core of User and Entity Behavior Analytics. It involves collecting and analyzing data from various log sources to understand the “normal behaviour” of users and entities within the network. Machine learning algorithms then identify deviations from these established patterns, flagging outlier events as anomalies. This process allows UEBA to detect suspicious activities that may indicate security incidents.
2. Data Integration
Data integration is crucial for enhancing the strength of existing security systems. User and Entity Behavior Analytics solutions integrate data from diverse sources, including logs, packet capture data, and other datasets, to provide a comprehensive view of network activities. This integration enables UEBA to correlate data across multiple organizational systems, improving the accuracy of anomaly detection.
3. Data Presentation
The presentation of data in User and Entity Behavior Analytics is vital for enabling security analysts and other stakeholders to interpret and make informed decisions based on the identified behaviour patterns. Visualization tools such as charts, graphs, and reports highlight anomalies, risk scores, and behaviour trends, facilitating efficient threat analysis and response.
How Do User and Entity Behaviour Analytics Work?
UEBA uses data analytics and machine learning to provide security insights. It ingests and analyzes large volumes of data from multiple sources to create a baseline of typical behaviours for users and entities. As machine learning models refine this baseline over time, they require fewer samples of normal behaviour to maintain accuracy.
User and Entity Behavior Analytics then apply these advanced analytics to current activity data, identifying suspicious deviations from the established baseline in real time.
Key data sources for User and Entity Behavior Analytics include:
- Network Equipment and Access Solutions: Firewalls, routers, VPNs, and IAM solutions.
- Security Tools and Solutions: Antivirus software, EDR, intrusion detection and prevention systems (IDPS), and SIEM.
- Authentication Databases: Active Directory and other databases containing critical information about user accounts and activities.
- Threat Intelligence Feeds: Frameworks like MITRE ATT&CK that provide information on common cyber threats and vulnerabilities.
- ERP and HR Systems: Systems containing pertinent information about users, including potential threats from disgruntled employees.
By analyzing this data, User and Entity Behavior Analytics identifies anomalous behaviour and scores it based on the risk it represents. For example, several failed authentication attempts in a short timeframe could indicate an insider threat and generate a low-scoring alert, while abnormal download patterns might suggest data exfiltration, warranting a higher risk score.
UEBA Use Cases
Insider Threats
Insider threats pose significant risks to organizations and can be classified into three types:
- Negligent Insider: An employee or contractor who unintentionally risks the organization’s security by not following proper procedures, such as leaving their computer unattended or failing to apply security patches.
- Malicious Insider: An employee or contractor who intentionally performs a cyber attack against the organization.
- Compromised Insider: An attacker who infiltrates the organization and compromises a privileged user account to continue the attack.
User and Entity Behavior Analytics excels in detecting these threats by establishing baselines for normal behaviour and identifying deviations. Traditional security tools often struggle with this, especially in the case of zero-day attacks or lateral movement within the organization.
Incident Prioritization
A Security Information and Event Management (SIEM) system collects events and logs from various security tools, generating numerous alerts. This can lead to alert fatigue among security staff. User and Entity Behavior Analytics helps prioritize incidents by identifying those that are particularly abnormal or dangerous, considering the organizational context. For instance, a minor deviation for a top-level administrator might be more concerning than a major deviation for a regular employee.
Data Loss Prevention and Data Leak Prevention
DLP tools aim to prevent the exfiltration of sensitive data but often generate high volumes of alerts. User and Entity Behavior Analytics can consolidate and prioritize these alerts by understanding which events represent anomalous behaviour compared to known baselines, helping investigators focus on real security incidents.
Entity Analytics (IoT)
The expansion of Internet of Things (IoT) devices introduces new security risks. User and Entity Behavior Analytics is crucial in managing these risks by tracking connected devices and establishing behavioural baselines for them. It can detect unusual activity, such as connections to unfamiliar addresses or unusual times of operation, indicating potential threats.
UEBA vs. SIEM
Security Information and Event Management (SIEM) is a technology that gives a detailed view of an organization’s security by using threat data and event information to identify normal and abnormal patterns. User and Entity Behavior Analytics (UEBA) works similarly by alerting security teams to anomalies but includes user and entity activity in its analysis.
In essence, SIEM focuses on security events, while User and Entity Behavior Analytics emphasizes behaviour patterns. A key difference is that SIEM is rule-based, meaning attackers can sometimes figure out and avoid SIEM rules. SIEM aims to catch threats immediately, but sophisticated attacks might unfold slowly over time.
UEBA, on the other hand, uses advanced algorithms and risk scoring instead of rules, allowing it to detect suspicious activity even if it occurs gradually. The best practice for IT security is to use both User and Entity Behavior Analytics and SIEM together for comprehensive threat detection.
What Are the Pros and Cons of UEBA?
User and Entity Behavior Analytics enhances an organization’s security by addressing gaps that traditional solutions like SIEM, user monitoring, and rule-based access control (RBAC) often miss. Combining UEBA with other security tools increases their effectiveness, reduces false positives, and identifies advanced threats.
Pros of UEBA:
- Automated Data Analytics:
- User and Entity Behavior Analytics tools collect and analyze large amounts of user and entity activity logs.
- These logs help score the risk of various security events, saving time and effort for security teams.
- Security analysts can focus on high-risk events instead of manually reviewing everything.
- Real-Time and Advanced Threat Detection:
- User and Entity Behavior Analytics can detect threats faster than traditional security tools, catching subtle behavioural changes before they break security rules.
- Early detection helps prevent incidents from escalating into serious problems.
- Automated Response:
- User and Entity Behavior Analytics tools can alert security teams and automatically block threats.
- This feature helps contain suspicious activity quickly, giving analysts time to investigate.
- Low Maintenance:
- Setting up User and Entity Behavior Analytics might be challenging, but it requires little maintenance afterwards.
- Once configured, UEBA operates with minimal oversight, using machine learning to adjust to changes.
Cons of UEBA:
- Building Behavioral Baselines:
- User and Entity Behavior Analytics requires extensive training and customization to learn normal user behaviour.
- This process can take up to three months, making UEBA a long-term strategy rather than a quick fix.
- Less Effective for Slow Attack Detection:
- User and Entity Behavior Analytics is best at detecting rapid changes in behaviour.
- Slow, subtle attacks, such as those by malicious insiders over a long period, may blend into normal behaviour and go unnoticed.
- Required Expertise:
- Training User and Entity Behavior Analytics needs specialized skills and knowledge.
- Organizations must prepare customized datasets, which are complex and require machine learning expertise.
- Hiring or training experts adds to the cost.
- High-Investment Deployments:
- Implementing UEBA involves significant time, effort, and costs for configuration, training, and integration.
- Employing AI specialists to manage User and Entity Behavior Analytics technologies further increases expenses.
All in all, while User and Entity Behavior Analytics offers advanced threat detection and automation benefits, it also comes with challenges such as high setup costs and the need for specialized expertise.
Frequently Asked Questions (FAQs)
Q 1. What’s the difference between UEBA and SIEM?
A. User and Entity Behavior Analytics focuses on detecting anomalies in user and entity behaviour using advanced analytics and machine learning. It identifies deviations from normal patterns that may indicate insider threats or compromised accounts. In contrast, SIEM collects and analyzes log data from various sources to provide real-time monitoring, threat detection, and incident response.
Q 2. How does UEBA handle false positives?
A. UEBA minimizes false positives through advanced analytics and machine learning algorithms. It establishes baseline behaviour patterns for users and entities, distinguishing normal variations from suspicious activities. By assigning risk scores to anomalies, User and Entity Behavior Analytics helps security teams prioritize alerts based on threat severity, reducing alert fatigue and improving incident response efficiency.
Q 3. What are the challenges in implementing UEBA?
A. Implementing User and Entity Behavior Analytics involves integrating data from diverse organizational sources such as logs, network traffic, and user activity. Building accurate behavioural baselines and training machine learning models requires time and specialized expertise. Initial setup costs include investments in AI specialists and integrating with existing security infrastructure.
Q 4. How effective is this against insider threats?
A. UEBA is highly effective in detecting insider threats, including negligent, malicious, and compromised insiders. By analyzing behavior across multiple systems, User and Entity Behavior Analytics identifies abnormal activities indicative of unauthorized access or data exfiltration. Early detection of deviations from normal behaviour helps organizations mitigate insider threats before they escalate into serious security incidents.
Q 5. What benefits does UEBA offer over traditional security monitoring tools?
A. UEBA provides real-time detection of anomalous behaviour using machine learning and behavioural analytics. It correlates data from various sources to uncover complex threats that traditional rule-based systems may miss. By prioritizing alerts based on risk scores, User and Entity Behavior Analytics reduces false positives and enables security teams to focus on critical threats, enhancing overall security posture and response capabilities.
Conclusion
User and Entity Behavior Analytics (UEBA) is now crucial in modern cybersecurity strategies. It helps detect advanced threats that traditional tools often miss by using machine learning and data analytics to establish normal behaviour patterns and spot unusual activities among users and entities in a network. This capability allows organizations to identify insider threats, prioritize incidents, prevent data breaches, and protect IoT environments effectively.
User and Entity Behavior Analytics offers several advantages, including real-time threat detection, automated responses to security incidents, and requiring minimal ongoing maintenance. However, implementing this system comes with challenges such as needing extensive training, customization efforts, and a significant initial investment. Despite these obstacles, the increasing importance of UEBA in cybersecurity highlights its role in enhancing organizational security and resilience against evolving threats.
