Ransomware is a type of malware that can prevent users from accessing their files by encrypting them or otherwise disrupting access to their data. Detecting ransomware-related behavior requires analyzing suspicious file activities, identifying behavioral patterns, and validating security evidence collected from monitored systems.
Traditional security monitoring relies on defined detection rules, endpoint events, and security indicators. However, analyzing suspicious file activity can require examining multiple behavioral characteristics, such as repeated file modifications, changes in file extensions, unusual file-access patterns, and unexpected activity within monitored directories.
In this use case, an isolated cybersecurity laboratory is established using VirtualBox, with Ubuntu configured as the endpoint monitoring environment and Kali Linux available as the authorized security-testing environment. Python is used to analyze a publicly available labeled dataset and perform controlled file-activity experiments on designated test files. Actual ransomware malware is not executed.
Wazuh is configured to collect relevant endpoint security events from Ubuntu. Where File Integrity Monitoring (FIM) is configured for the designated test directory, changes to monitored files are collected and reviewed as endpoint evidence. The dataset analysis and the Wazuh endpoint-monitoring results are analyzed as separate evidence sources because they represent different data and testing conditions.
Python is used to preprocess the dataset, examine relevant behavioral features, analyze collected event records, and evaluate the results supported by the available labels and evidence. Qwen2.5 assists with interpreting structured findings and summarizing observed patterns; it is not treated as a trained ransomware classifier unless a separate, validated classification model is implemented.
MITRE ATT&CK is used to map observed behaviors to relevant techniques only when the collected evidence supports the mapping. The results are reviewed to distinguish dataset-based analytical findings from endpoint-monitoring observations, and the final report documents the methodology, evidence, limitations, and outcomes.
Publicly Available Labeled Dataset → Python-Based Behavioral Analysis → Qwen2.5-Assisted Evidence Interpretation → Ubuntu Endpoint Monitoring → Controlled File-Activity Assessment → Wazuh Event Collection → Evidence Validation → MITRE ATT&CK Technique Mapping → Result Evaluation → Security Investigation Report
Application/Service: Ubuntu Endpoint File System and Security Monitoring Environment
Ubuntu provides the endpoint environment used to perform controlled file activities and collect security-related evidence. Its files and directories can be monitored to identify changes that may be relevant to an investigation.
Wazuh provides endpoint security monitoring and event collection. When the required agent configuration and File Integrity Monitoring settings are enabled, changes to designated files can be recorded for investigation.
Ransomware-related file activity can involve repeated modifications, file creation, file renaming, and changes to file extensions. If these activities are not monitored or their events are not reviewed effectively, suspicious behavior may be overlooked.
A dataset can contain labeled examples of ransomware-related or benign behavior, but its patterns cannot automatically be assumed to match events generated by a separate Ubuntu laboratory. The dataset and endpoint monitoring therefore require separate analysis and evidence validation.
The proposed solution introduces dataset-based behavioral analysis, controlled file-activity testing, Wazuh endpoint monitoring, structured evidence interpretation, behavior-based technique mapping, and separate evaluation of analytical and endpoint-monitoring results.
Specific Attack: Controlled Ransomware-Like File-Activity Assessment
The controlled scenario examines file activities that may resemble selected ransomware-related behaviors without executing ransomware malware or encrypting real user data.
A designated Ubuntu test directory contains disposable files created specifically for the experiment. Python performs controlled and reversible operations, such as creating test files, modifying their contents, renaming selected files, and changing test-file extensions. These activities are limited to the designated laboratory directory.
Wazuh is configured to monitor the designated directory where supported by the active File Integrity Monitoring configuration. The resulting events are collected and reviewed to determine which controlled changes were recorded.
The dataset-based analysis is performed separately using the available labeled records. The observed patterns from the dataset are not treated as proof that the Ubuntu endpoint has experienced a ransomware attack.
Behavior-Based Ransomware Analysis and Endpoint Security Monitoring
The primary security concept is Behavior-Based Ransomware Analysis combined with Endpoint Security Monitoring.
The analysis examines ransomware-related behavioral patterns in a publicly available labeled dataset and separately monitors controlled file activities in an Ubuntu endpoint environment.
Python is used to analyze dataset features and process collected event records. Wazuh monitors the configured Ubuntu test directory and collects relevant endpoint events. Qwen2.5 assists with interpreting structured findings, while the original evidence is used to validate its output.
The security concept defines the overall analysis and monitoring strategy.
The following are the specific defensive mechanisms implemented.
Python is used to preprocess the publicly available labeled dataset and examine relevant behavioral features. Analysis is limited to what the selected dataset and its labels support.
Disposable files are used to perform reversible file operations within a designated Ubuntu laboratory directory.
Wazuh collects relevant endpoint security events from the configured Ubuntu environment. File Integrity Monitoring is configured for the designated test directory where supported.
The file operations performed during the experiment are compared with the events recorded by Wazuh.
Qwen2.5 is used to help interpret structured findings and produce summaries based on the evidence supplied to it. Its output is checked against the original records.
MITRE ATT&CK is consulted to identify relevant techniques supported by validated observations.
Dataset analysis and Wazuh endpoint-monitoring results are evaluated independently.
Dataset findings, endpoint events, analytical results, limitations, and validation evidence are recorded.
Python is used to preprocess the publicly available labeled dataset, examine relevant features, process structured event records, perform controlled file operations, and calculate evaluation results where appropriate.
Wazuh is used to collect and review security-related events from the Ubuntu endpoint. File Integrity Monitoring is configured for the designated laboratory directory, where supported by the deployed configuration.
Qwen2.5 is used to assist with the interpretation of structured dataset findings and endpoint-event summaries. It does not automatically become a trained ransomware detection model merely by being included in the workflow.
MITRE ATT&CK provides a knowledge base of adversary tactics and techniques. Relevant entries are selected only when supported by the observed behavior.
Ubuntu provides the endpoint environment where controlled file activities are performed and Wazuh monitoring is evaluated.
Kali Linux provides an optional security-testing environment within the isolated laboratory. It is used only when an authorized test or validation task requires activity originating from the security-testing system. It is not required for Python dataset processing or for performing local file operations on Ubuntu.
VirtualBox provides the virtual-machine infrastructure used to isolate the laboratory systems.
A controlled cybersecurity laboratory is established using VirtualBox, with Ubuntu providing the endpoint environment and Kali Linux available for authorized security testing where required.
A publicly available labeled dataset is preprocessed and analyzed using Python to identify behavioral patterns represented in the available records.
Relevant dataset findings are documented with reference to the selected features, available labels, and limitations of the dataset. Evaluation metrics are calculated only when supported by the implemented analytical task.
Controlled file activities are performed on disposable test files within the designated Ubuntu directory without executing actual ransomware or modifying real user data.
Wazuh collects endpoint events from the configured Ubuntu environment, allowing the recorded file changes to be reviewed as security evidence.
The expected file activities are compared with the observed Wazuh events to establish which controlled operations were recorded and to identify potential monitoring gaps.
Dataset-based analytical findings and Wazuh endpoint-monitoring results are evaluated independently, preventing dataset performance from being misrepresented as proof of live endpoint detection.
Qwen2.5 assists with interpreting structured evidence and preparing investigation summaries, while its outputs are checked against the original dataset records and endpoint events.
Relevant MITRE ATT&CK techniques are identified only when the validated behavioral evidence supports the mapping.
The final investigation report documents the dataset analysis, controlled file-activity assessment, endpoint events, evidence validation, analytical results, limitations, and recommendations for further testing.
The complete AI-assisted ransomware behavior analysis and endpoint-monitoring workflow is demonstrated in a controlled laboratory, with findings limited to the evidence collected and the capabilities actually implemented.
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