Behavioral analytics: detect user behavior anomalies

The precise and continuous monitoring of user behaviors has become an essential lever for enhancing the cybersecurity of organizations. Behavioral analysis, leveraging advanced algorithms and predictive models, offers unprecedented capabilities for real-time detection of user anomalies, allowing for the anticipation and effective countering of internal and external threats. At the heart of this discipline, behavioral monitoring stands out as a key tool for identifying deviations from established habits, thereby revealing subtle indicators of compromise or fraud.

In the face of the increasing complexity of information systems and the growing volumes of data, behavioral analysis technologies now rely on advancements in machine learning to process, classify, and interpret signals arising from human interactions as well as machines. These predictive models not only allow for the creation of accurate symbolic profiles of legitimate behaviors but also for the detection of atypical activity sequences, often invisible to classical systems. Integrating these solutions into security infrastructures, particularly through SIEM platforms, significantly enhances responsiveness to incidents.

In brief:

• Behavioral analysis leverages user data to identify behavioral discrepancies.
• User anomalies are detected through statistical models and machine learning algorithms.
• A real-time behavioral monitoring optimizes detection and response to intrusions.
• Fraud detection relies on predictive analysis to anticipate malicious patterns.
• Collaboration between UEBA and SIEM strengthens the overall security posture of organizations.

Fundamentals of Behavioral Analysis for Detecting User Anomalies

Behavioral analysis aims to understand and model the individual and collective habits of users within an IT system. This involves collecting vast datasets from activity logs, network traffic, and application interactions, to establish a behavioral baseline or “profile” for each entity. The system then detects any significant deviation from this profile, termed an anomaly.

Anomalies are generally categorized into three distinct types, each corresponding to a specific type of user behavior deviation: temporal anomalies, account anomalies, and pattern anomalies.

Temporal anomalies occur when a user’s actions take place at unusually different times from the norm. For example, an employee who typically accesses the system between 9 AM and 6 PM and suddenly logs in at 3 AM may trigger an alert. This type of anomaly is often associated with unauthorized access attempts outside business hours.

Account anomalies correspond to an unusual amount of actions on an account within a short timeframe, such as a high number of modifications or accesses to sensitive data. This form of anomaly is frequently observed in cases of compromise where an attacker attempts to quickly extract a large volume of information.

Pattern anomalies involve sequences of incoherent or suspicious actions, like the success of a login after multiple failures, followed by multiple file deletions—a pattern often associated with insider attacks or credential theft.

These different anomalies are detected through a combination of statistical models and machine learning algorithms that continuously analyze the collected data. The algorithm learns the normal behavior over an initial period, thus establishing a personalized baseline for each user and device. Notable deviations allow for the assignment of a risk score, which guides investigation priority for security teams.

This mechanism presents several undeniable advantages: it exceeds mere event correlation to adopt a contextual analysis, significantly reduces false positives due to better understanding of habits, and offers the capacity for gradual adaptation to new or changing behaviors.

Key Technologies and Essential Components of UEBA in Cybersecurity

UEBA (User and Entity Behavior Analytics) incorporates several components that are predominantly technological and analytical to ensure reliable detection of behavioral anomalies.

At the core of these solutions, the collection and integration of diversified data from heterogeneous sources such as access logs, network flows, system events, endpoints, and even cloud applications are indispensable. Enriching data with specific metadata (user, role, geographical location) increases the relevance of analyses. These data are centralized in SIEM platforms, which play a pivotal role by providing a unified repository.

The analysis then relies on the exploitation of advanced algorithms (supervised and unsupervised machine learning, statistical techniques) that identify trends, anomalies, and implicit correlations that are undetectable by human actions or static rules. For instance, robust principal component analysis (RPCA) highlights significant deviations, while Markov chains model the probability of action sequences.

An innovative aspect of these systems is the ability to carry out individualized risk scoring. This score synthesizes the severity and frequency of detected anomalies and guides analysts in their prioritization responses. Furthermore, the clear and human-friendly visualization of results through interactive dashboards facilitates interpretation and reactivity.

The convergence between UEBA and SIEM contributes to the strengthening of security measures. Gartner emphasizes this essential synergy where UEBA complements SIEM event correlation by focusing its analyses on user and entity behavior to detect internal threats or zero-day attacks often invisible to classical solutions.

It is essential that the solutions remain flexible and adaptable. Customizable anomaly models allow for better alignment with the specific contexts of businesses, including, for instance, the notion of seasonality in behaviors or dynamic grouping into peer groups.

Real-Time Behavioral Analysis: A Major Advancement for Anticipating Cyber Threats

Real-time analysis revolutionizes the capabilities for identifying abnormal behaviors by offering immediate threat detection before they can cause significant damage. Unlike traditional batch analysis, this approach relies on continuous processing, powered by technologies like Apache Kafka or Amazon Kinesis, capable of handling a constant flow of multi-source data.

This method includes the construction of predictive models based on various machine learning techniques ranging from statistical analysis to deep neural networks. It also remarkable incorporates the use of language models (LLMs) for fine interpretation of logs expressed in natural language, improving the understanding of the operational context.

The complete cycle includes collection, modeling, detection, and scoring of anomalies, as well as implementing a rapid automated response, integrated within SIEM or SOAR platforms.

These systems trigger early alerts, which allows, for example, the automatic blocking of a suspicious user account or the isolation of a compromised server. The feedback mechanism also allows for continuous refinement of models based on the outcomes of actions taken.

Beyond cybersecurity, this technology is particularly effective in the financial sector for detecting transaction fraud, in e-commerce to detect bots or optimize customer personalization, thereby contributing to enhanced security and a better user experience.

Practical Applications and Industrial Use Cases

A concrete use case can be envisaged in a financial institution that is a victim of a sophisticated attack attempt. UEBA coupled with real-time analysis detects a user who, typically absent during off-peak hours, suddenly sends requests to sensitive customer databases at 4:45 AM. The platform immediately notifies security teams who can initiate isolation measures, thereby preventing a massive data breach.

In another context, an e-commerce site can utilize ABTR (real-time behavioral analysis) to detect that a customer hesitates in their purchasing process and automatically triggers a personalized incentive to complete the sale or offers assistance via chatbot.

How to Select an Appropriate UEBA Solution: Essential Technical and Functional Criteria

Choosing a high-performing UEBA solution requires consideration of several fundamental criteria:

1. Real-time alerts: the ability to generate instant notifications when an anomaly is detected is critical for increased responsiveness.
2. Multi-source collection: the system must efficiently aggregate logs, network flows, IoT data, without impacting network performance or requiring complex agent deployment.
3. Model customization: the solution must allow for the creation or adaptation of anomaly models based on operational and business specifics.
4. Actionable reports: the clear presentation of results, particularly through dashboards with detailed scoring, facilitates decision-making and adjustment of rules.
5. Accurate risk assessment: assigning a score to suspicious behavior allows for effective sorting of alerts and optimizes human resources dedicated to cybersecurity.
6. Peer group analysis: comparing a user to their peers helps detect subtle deviations that might otherwise go unnoticed.

These criteria help align business needs with technical capabilities, thereby ensuring the best fit with security and compliance constraints.

A table summarizes the differences and objectives of the main security systems integrated with UEBA and highlights the importance of this convergence.

What is the difference between UBA and UEBA?

UBA focuses solely on the behavioral analysis of human users, while UEBA extends this analysis to non-human entities such as servers, applications, and network devices. This allows for a more comprehensive detection of internal and external threats.

How does UEBA minimize false positives?

UEBA learns the usual behaviors of each user over a given period, enabling it to recognize significant deviations and avoid alerts caused by normal or seasonal variations.

Does behavioral analysis replace traditional security systems?

No, it complements existing systems such as SIEMs, firewalls, and EDR solutions, providing in-depth behavioral analysis that improves detection of hard-to-identify threats using static rules.

What are the ethical challenges associated with the implementation of UEBA?

They relate to privacy protection, transparency in data use, and prevention of abusive profiling. It is essential to establish a clear ethical framework to ensure responsible use.