What is User Behavior Analysis in threat detection?
User Behavior Analysis (UBA), also known as User and Entity Behavior Analysis (UEBA), is an approach used in cybersecurity to identify, monitor, and analyze the behavior of users and entities within the organization or a network. The major goal of UBA is to identify the possible vulnerabilities caused by the entities within an organization. It can also help detect and respond to anomalies, security threats, and suspicious activities in the normal functioning of the organization’s functions.
Let’s dive into the process of UBA and how it can benefit the threat detection process.
Components of UBA
The key working components of UBA need to be discussed before we move further in the process of UBA. These components are used in the process to ease up the threat detection procedure.
User profiling: This is defining baseline user behavior within that organization. This baseline behavior serves as the typical behavior of the user. The behavior metrics include the work hours, applications in use, and in-use devices of the user.
Anomaly detection: The deviation from the user’s baseline is considered an anomaly or suspicious activity on the user’s account. The process continuously monitors the user activity and compares it against its baseline defined. An example of an anomaly can be an unusual time of log in from the user’s account, use of a new device, or unusual access requests.
Contextual analysis: Considering the user’s working context is important for concluding the anomalies on the user’s account. The content information can include the department, location, understanding of the assigned privileges, and the user’s IP address. For example, a user working in the Argentina Time Zone will have different working hours than one working in the Eastern Time Zone.
Machine learning: This process includes using machine learning and artificial intelligence models to keep track of user activity. It can help in easy pattern recognition and anomaly detection. It helps adapt to new and extensive user data more easily and recognize abnormal behaviors.
Implementation of UBA
To use the components of UBA effectively in threat detection, we need to define the basic structure for implementing UBA within an organization:
Data aggregation: The process of identifying the data sources for the UBA. The common sources include logs from servers, databases, endpoints,
SIEM solutions Real-time monitoring, analysis, rseponse to the incidents and events occurred in an organization. Choosing the ML algorithm: Using the right machine learning algorithm according to the organization’s needs can help identify anomalies and deviations in the normal working of user operations. It provides an easy way to classify the action as normal or anomalous.
Integrating with security solutions: The organizations have their own security mechanism for ensuring the other security parameters. It is important to ensure the integration of UBA in our security mechanism doesn’t hinder the security implementation. The security implementation tools include organization’s SIEM, EDR, automation, and response solutions.
Compliance check: Ensure that the UBA implementation in organization’s security system complies with the relevant industry’s data privacy and security regulations.
Use of user profiling: Next, we actively make baseline profiles of all our active users in the organization. We continuously monitor the user’s activity, gather historical data facts about the user’s operations, and make them standards for detecting anomalies in the system.
Incident response plan: For the incident response, we define thresholds for alerting, assign priority to the generated logs and demand immediate action in case of high-priority alerts. It’s also important to define an Incident Response (IR) plan, defining the plan needed to be executed in case a UBA alert is triggered.
Documentation: Generate effective and descriptive reports of all the incidents within the organization and how the UBA resolved them. The documentation also includes the context of the incident, like the user involved, time, location, and what resources were used.
Implementing all these steps can help in effectively implementing UBA within the system. UBA helps define a measure to detect and respond to insider threats and incidents.
Benefits of UBA
Using UBA in an organization’s security metric proves to be an effective security metric. Other than that, it also has some other benefits that further enhance the threat detection process.
Early threat detection: UBA goes beyond traditional threat detection like signature-based recognition and offers early-stage identification of threats in an organization. This helps in containing the threat damage before the damage escalates.
Minimized false positive results: UBA uses baselines and advanced ML techniques to reduce the number of false positive results. Context analysis capability of UBA helps in reducing false positive results. This helps in focusing the attention on genuine security threats.
Insider threat detection: UBA identifies anomalous user behavior of the organization’s insiders. The insiders within an organization are the greatest security threats and cause the maximum number of vulnerabilities.
Adaptive security: UBA offers adaptive security by adapting to new AI and ML tactics and techniques with changing threats, vulnerabilities, and attack surface area. This is an effective measure to detect the zero-day vulnerabilities in an organization.
These components and benefits of using UBA make it a valuable component to include in your organization’s security strategy. This helps in proactively defending against a wide range of security threats.
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