Excessive Failed Logins for a Single User
Goal
The objective of this predictive AI model is to proactively identify brute force attack attempts against critical systems and services. Unlike traditional rule-based detection mechanisms that only trigger once suspicious activity is already underway, the predictive model stablish behavioural profiles and analyzes historical data to anticipate potential brute force activity before it fully materializes.
By leveraging machine learning algorithms, the model continuously monitors compare authentication events, login attempts, and traffic anomalies to behavioural pattern profiles to detect early indicators of brute force techniques, such as:
Unusual spikes in failed login attempts.
Abnormal authentication behavior across multiple accounts, IP ranges, or geographies.
Patterns consistent with automated tools or distributed attack campaigns.
The ultimate goal is to enable security teams to detect and respond earlier, reducing the window of exposure and minimizing the risk of unauthorized access.
This predictive AI model is designed to detect brute force and targeted account takeover attempts where an attacker repeatedly attempts to guess or validate the credentials of a specific user account.
The model continuously monitors authentication events and analyzes the volume, frequency, and timing of failed login attempts associated with each username. By establishing a behavioural baseline of what constitutes normal login behavior for individual accounts, the model can accurately identify when a sudden surge of failed attempts indicates malicious activity rather than a legitimate user error.
This predictive approach enables the system to:
Recognize targeted attacks against privileged or high-value accounts (e.g., administrators, executives).
Differentiate anomalies between ordinary failed attempts (such as a user forgetting their password) and coordinated brute force campaigns.
Generate early warnings that an account is under attack, before a successful compromise occurs.
Support compliance and best practices by enforcing strong access monitoring as required by frameworks such as NIST, ISO 27001, or PCI DSS.
The ultimate goal of the model is to ensure that user accounts remain secure against credential-guessing attacks, while at the same time minimizing false positives and reducing the operational burden on SOC analysts.
Description
This predictive AI model operates by analysing authentication activity logs in real time and extracting relevant features from specific fields that are consistently present in most identity and access systems. The key input fields are:
eventId (or equivalent): Provides the unique identifier and classification of the authentication event, allowing the model to distinguish between login attempts, password resets, or other access-related actions.
login_result: Indicates the outcome of each authentication attempt (e.g., success, failure). This is critical for recognising abnormal patterns of repeated failures that may signal brute force attempts.
userName: Identifies the account being targeted. Monitoring the frequency and distribution of login attempts per user helps the model detect suspicious behavior such as attempts against multiple accounts or specific high-value users.
sourceAddress: Captures the origin of the authentication request. By correlating login activity across IP addresses, the model can identify distributed attacks, anomalous geographies, or suspicious address ranges.
The model continuously processes these fields to establish behavioural baselines for each user, source IP, and event type. Machine learning techniques are applied to detect deviations from normal activity, such as:
A sudden spike in failed login attempts from a single source IP.
Multiple failed attempts distributed across many usernames from the same IP or subnet.
Repeated failed attempts against a single user from different geographic locations.
Unusual event sequences (e.g., rapid succession of failures followed by a success).
By correlating these signals, the model generates a predictive risk score for each authentication event. Events with a high probability of representing a brute force attempt are escalated as alerts to the SOC, enabling analysts to take preventive action before account compromise occurs.
This predictive AI model leverages authentication activity logs and specifically analyzes four key fields:
eventId (or equivalent): Identifies the type of event, allowing the model to focus on authentication attempts and differentiate them from other system events.
loginResut: Indicates whether each authentication attempt was successful or failed. The model uses this field to build a sequence of login outcomes for every user.
userName: Serves as the central pivot for analysis. The model tracks authentication behavior per individual user, detecting when the number of failed attempts deviates from the user’s normal baseline.
sourceAddress: Provides context about the origin of the login attempts. By correlating failed logins across different IPs, the model can distinguish between a single user mistyping a password multiple times (usually from one device/IP) versus a distributed attack where multiple IPs target the same account.
The model functions by establishing a behavioral baseline for each user’s login activity. Under normal conditions, users may generate occasional failed logins, but these are typically low in frequency and occur in predictable patterns (e.g., one or two failures before a success).
Using machine learning techniques, the model detects anomalous patterns, such as:
A sharp increase in the number of failed logins for the same username within a short time window.
Repeated failures against privileged accounts (administrators, executives) regardless of the source IP.
Coordinated attempts from multiple IP addresses targeting the same username simultaneously.
“Low and slow” strategies, where failures are spread over longer periods to avoid threshold-based detection.
When such anomalies are detected, the model calculates a predictive risk score for the account. A high score indicates a high likelihood of an ongoing brute force or credential-stuffing attempt specifically targeting that user. These events are escalated as signals within the SOC.
By correlating authentication failures at the user level, the model provides early visibility into targeted attacks, ensuring that high-value accounts remain protected and that SOC teams can intervene before compromise occurs.
Characteristics
Type
Network
Data source
Authentication
Required fields
event_id (or equivalent)
Login_result
username
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