Identity and Access Management (IAM) platforms control user authentication and access to protected applications. Keycloak is an open-source IAM platform that provides user authentication, identity federation, single sign-on, and access-management capabilities. It records authentication-related events that can be used to examine login activity and investigate suspicious account-access behavior.
Account-takeover attacks occur when an unauthorized person gains access to a legitimate user's account. Such activity may involve the use of compromised credentials, authentication from unfamiliar clients, unusual login sequences, or changes in previously observed login behavior. A login attempt using valid credentials may still be suspicious if its surrounding behavioral characteristics differ from the account's normal activity.
Conventional authentication mechanisms primarily verify credentials and apply configured authentication policies. Although these controls are essential, credential verification alone does not establish whether a login attempt originates from the legitimate account owner. Sequential authentication analysis provides an additional approach for identifying suspicious patterns across multiple login events.
In this use case, Keycloak is deployed in an isolated Ubuntu and Kali Linux laboratory environment. Keycloak manages designated test accounts and generates authentication events during normal operations and controlled security testing.
An existing public authentication-event dataset is used as the data source for machine-learning training and evaluation. The dataset is inspected to identify available authentication attributes and account-takeover labels. Suitable records are processed to construct sequential login features for the Transformer model.
A Transformer-based classification model is trained using the selected labelled authentication sequences. The model learns relationships between events in a sequence and generates predictions for authentication behavior that may be associated with account-takeover activity.
A controlled account-takeover scenario is then reproduced against the laboratory Keycloak environment. Authentication events are collected from the controlled scenario and processed through a compatible feature-extraction pipeline. The trained Transformer model evaluates the resulting login sequence and generates a classification prediction.
The model prediction is compared with the known laboratory test condition to evaluate the detection workflow. The Transformer model operates as a separate Python-based detection component; it is not presented as a built-in Keycloak feature.
Application : Keycloak Identity and Access Management
Keycloak is the target IAM application in this use case. It provides authentication and identity-management functionality for designated laboratory users and client applications.
Keycloak is deployed only within the authorized laboratory environment so that normal authentication activity and controlled suspicious login scenarios can be generated and analyzed.
Keycloak verifies authentication requests according to its configured authentication flows, credentials, and security policies. However, a valid authentication event does not necessarily establish that the person attempting to access the account is its legitimate owner.
If an unauthorized person obtains valid credentials, the authentication request may satisfy the configured credential-verification requirements. Suspicious behavior may instead become apparent through a sequence of events, such as unusual login timing, changes in client attributes, or an unexpected pattern of authentication outcomes.
Without an additional behavioral-analysis component, suspicious sequences may require further examination by a security analyst.
Keycloak IAM
↓
User Authentication Request
↓
Configured Credential Verification
↓
Authentication Event Generation
↓
Potential Use of Compromised Credentials
↓
Unauthorized Account-Access Attempt
↓
Suspicious Login-Sequence Characteristics
↓
Potential Account-Takeover Activity
↓
Requirement for Sequential Behavioral Analysis
The proposed solution introduces a separate Transformer-based detection component that analyzes collected Keycloak authentication events. The model learns patterns from an existing public authentication-event dataset and evaluates compatible login sequences for behavior associated with account-takeover activity.
Specific Attack : Controlled Account-Takeover Attempt Against Keycloak IAM
The controlled attack scenario evaluates whether the proposed detection workflow can identify suspicious authentication sequences generated against Keycloak.
A designated test account is used to establish normal authentication behavior. A
separate controlled scenario is then conducted to represent suspicious account access, using an unfamiliar laboratory client or an unusual sequence of authentication attempts.
The test is restricted to the isolated Keycloak environment. It does not target real users, production identity systems, or external accounts, and it does not require the use of real stolen credentials.
The purpose is to generate authentication-event evidence that can be collected, transformed into sequential features, analyzed by the trained Transformer model, and compared with the known laboratory test condition.
The public dataset supplies the existing labelled records used for machine-learning development. The controlled Keycloak scenario provides separate laboratory evidence for evaluating the detection workflow.
Transformer-Based Sequential Authentication Analysis for Keycloak Account-Takeover Detection
The primary security concept combines sequential authentication-event analysis with Transformer-based classification to identify login behavior that may be associated with account-takeover activity in Keycloak.
Authentication-Event Data Preparation : Existing public authentication-event data is inspected to identify usable fields and labels. Suitable records are cleaned, transformed, and organized into sequences for model training and evaluation.
Transformer-Based Sequential Classification : The Transformer model learns relationships between authentication events within a sequence. It evaluates the prepared sequence and produces a prediction based on the patterns learned from the training data.
Keycloak Event Analysis and Validation : Authentication events generated by Keycloak during normal and controlled suspicious activity are collected and processed using a compatible feature-extraction pipeline. The model prediction is compared with the documented laboratory test condition to evaluate whether the detection workflow identifies the intended suspicious behavior.
The security concept defines the overall security strategy.
The following are the specific defensive mechanisms implemented.
Authentication events are organized into sequences using available timestamps, account-related identifiers, and compatible event attributes. The sequences are analyzed by the trained Transformer model.
The trained Transformer model evaluates a processed login sequence and generates a classification prediction.
The public training data and newly collected Keycloak events are processed using compatible feature-extraction procedures.
The Transformer prediction is treated as an additional signal for evaluating whether an authentication sequence requires further review.
Login sequences classified as suspicious are recorded and presented for security review.
Relevant Keycloak authentication events are collected during normal activity and controlled security testing.
Keycloak provides the identity and authentication environment used for the account-takeover detection experiment.
Ubuntu hosts Keycloak and supports the data-processing and Transformer-based detection components.
Kali Linux is used as the controlled testing system for sending authorized authentication requests to the laboratory Keycloak instance.
VirtualBox provides the isolated virtual-machine environment for the application and testing systems.
Python provides the main implementation environment for dataset processing, sequential feature extraction, Transformer training, classification, and evaluation.
Pandas is used to load, inspect, filter, and organize the existing public authentication-event data and collected Keycloak events.
NumPy supports numerical operations required for feature preparation and model-data processing.
PyTorch is used to implement and train the Transformer-based classification model.
Scikit-learn provides utilities for data partitioning and classification evaluation.
Keycloak's authentication-event logging provides the event evidence used by the detection workflow. The exact event fields depend on the Keycloak configuration and the collection method selected for the laboratory.
cURL is used to send controlled HTTP requests to the laboratory authentication environment where appropriate.
OWASP ZAP can be used to inspect HTTP traffic exchanged with the authorized laboratory application.
SQLite stores the relevant laboratory authentication records and associated model predictions.
Streamlit provides an interface for displaying authentication sequences, Transformer predictions, and evaluation results.
Keycloak Identity and Access Management is deployed within an isolated Ubuntu and Kali Linux laboratory environment to provide the specific target application for account-takeover detection research.
An existing public authentication-event dataset is inspected and processed to identify the available login attributes and labelled records suitable for sequential machine-learning analysis.
Authentication records are transformed into sequential numerical features, providing a consistent representation for Transformer model training and inference.
A Transformer-based classification model is trained using the selected labelled authentication sequences and evaluated using records that were not used during training.
The model's classification performance is assessed using precision, recall, F1-score, and confusion matrix, providing measurable evidence of its ability to distinguish the available authentication categories.
Normal authentication activity and a controlled suspicious-login scenario are generated against the laboratory Keycloak instance, providing authentication-event evidence for evaluating the detection workflow.
The collected Keycloak events are processed using the compatible feature-extraction procedure and analyzed by the trained Transformer model to generate predictions for the observed login sequences.
The model predictions are compared with the known laboratory test conditions, allowing the experiment to document detected suspicious sequences, false positives, and false negatives.
SQLite stores relevant authentication events and model predictions, while Streamlit presents the sequential login information and detection results for review and verification.
The complete workflow demonstrates the integration of public authentication-data processing, Transformer-based sequential login analysis, Keycloak authentication testing, controlled account-takeover validation, measurable model evaluation, and evidence-based security review within an isolated laboratory environment.
Research Breakthrough Possible @S-Logix