Advanced SOC Simulation is a structured cybersecurity investigation project designed to simulate a real-world Security Operations Center (SOC) case involving suspicious API activity within a factory monitoring web application.
The project demonstrates the complete lifecycle of a security investigation, including detection, analysis, incident documentation, risk evaluation, threat mapping, and security maturity assessment.
The primary objective of this simulation is to identify and analyze abnormal behavior performed by an authenticated internal user. The activity involves wildcard API queries, cross-factory data access, and sequential machine enumeration, which indicate potential reconnaissance or misuse of legitimate credentials.
This project is structured to reflect enterprise-level security documentation standards and operational workflows. It showcases practical skills in log analysis, behavioral detection engineering, incident response, and risk management.
The simulation emphasizes a key security principle: authentication alone does not eliminate risk. Monitoring authenticated behavior is essential to detecting insider threats and compromised accounts.
This project is intended to demonstrate hands-on SOC capabilities aligned with modern security operations and threat detection practices.
During routine monitoring of a factory monitoring web application, unusual API activity was identified originating from an internal IP address.
The user successfully authenticated into the system and began issuing multiple API requests within a short time window. The observed behavior included:
- Wildcard queries using broad parameters (e.g., factory=, machine=)
- Access to multiple factory environments
- Sequential requests targeting individual machines
- Rapid API interactions immediately after login
Although no brute-force attempts or authentication failures were detected, the post-authentication activity pattern was inconsistent with normal operational use.
The behavior suggests potential internal reconnaissance, misuse of legitimate access privileges, or possible credential compromise.
No evidence of data modification or service disruption was observed. However, the scope and pattern of data retrieval indicate elevated risk due to exposure of operational infrastructure details.
This scenario was analyzed using a structured SOC investigation approach, including log review, detection rule development, incident documentation, risk assessment, and threat mapping.
The project is organized to reflect enterprise-level security documentation standards and structured investigation workflows.
SOC-SIMULATION
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├── README.md
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├── data/
│ └── log-data.log
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├── docs/
│ ├── 01_Executive_Summary.md
│ ├── 02_Environment_Architecture.md
│ ├── 03_Log_Analysis.md
│ ├── 04_Detection_Logic.md
│ ├── 05_Incident_Report.md
│ ├── 06_Risk_Assessment.md
│ ├── 07_MITRE_Mapping.md
│ ├── 08_Response_Workflow.md
│ └── 09_Security_Maturity_Assessment.md
│
└── evidence/
├── suspicious_queries.txt
├── high_activity_ip.txt
└── log_sample.txtRaw log data used for analysis and detection validation is documented below:
This file contains the original structured log dataset that was analyzed during the SOC simulation.
It includes:
- IP addresses
- Timestamps
- HTTP methods
- API request paths
- Authentication status
- User session identifiers
The log data served as the primary source for:
- Suspicious behavior detection
- API enumeration analysis
- Risk assessment
- Incident documentation
- Threat mapping to MITRE techniques
Preserving raw log evidence ensures transparency, reproducibility, and forensic validation of the investigation process.
A brief overview of the project investigation and findings is documented below:
This section summarizes the SOC simulation conducted on a factory monitoring web application. The investigation focused on detecting suspicious authenticated API activity involving wildcard queries, cross-factory access, and machine enumeration.
Key findings include:
- Successful authentication followed by abnormal API usage
- Detection of potential reconnaissance behavior
- High-risk activity without system compromise
- Identified gaps in API access control and monitoring
The project demonstrates structured log analysis, threat detection, risk assessment, and incident documentation following enterprise SOC practices.
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docs/
Contains all structured investigation documentation, including analysis, detection logic, incident reporting, risk assessment, and maturity evaluation. -
evidence/
Stores extracted log data, suspicious query records, and forensic artifacts used during analysis. -
assets/
Contains visual diagrams supporting architectural understanding, incident workflow, and MITRE ATT&CK mapping.
This structure separates analytical documentation, forensic evidence, and visual assets to reflect professional SOC project organization.
Detailed architecture documentation is available in the following file:
View Environment Architecture Documentation
This document explains:
- Application architecture overview
- Log generation flow
- Monitoring components
- Detection pipeline design
- SOC analysis workflow
The architecture reflects a simulated enterprise web monitoring environment used for security investigation and behavioral analysis.
Detailed log investigation findings are documented in:
View Log Analysis Documentation
This document includes:
- Log format breakdown (Apache access logs)
- IP activity summary
- Suspicious query identification
- Wildcard API usage analysis
- Sequential machine access patterns
- Behavioral anomaly indicators
The log analysis focuses on identifying abnormal authenticated activity and distinguishing normal operational behavior from potential reconnaissance patterns.
Detection engineering details are available in:
View Detection Logic Documentation
This document includes:
- Suspicious behavior detection criteria
- Wildcard query detection rules
- High-frequency API request thresholds
- Cross-factory access monitoring logic
- Alert classification framework
- Recommended SIEM rule structure
The detection logic demonstrates how behavioral monitoring can identify insider threats or compromised accounts even when authentication is successful.
The formal incident documentation is available here:
This report includes:
- Incident identification details
- Timeline of suspicious activity
- Affected systems and scope
- Investigation findings
- Severity classification
- Containment and remediation recommendations
- Final SOC conclusion
The report is structured using enterprise SOC documentation standards to reflect real-world incident handling practices.
The risk evaluation and impact analysis are documented here:
This document includes:
- Asset identification and sensitivity evaluation
- Threat and vulnerability assessment
- Likelihood vs Impact analysis
- Risk rating classification
- Business impact considerations
- Mitigation recommendations
The assessment follows a structured risk evaluation approach to determine the potential organizational impact of the identified suspicious activity.
Threat technique mapping based on investigation findings is available here:
This document includes:
- Identified attacker behaviors
- Relevant MITRE ATT&CK tactics
- Technique mapping (Discovery, Credential Access, etc.)
- Behavioral justification for mapping
- Detection coverage analysis
- Gaps in current monitoring capability
The mapping aligns observed suspicious activity with globally recognized adversary techniques to enhance threat intelligence context and detection maturity.
The structured response handling process is documented here:
This document includes:
- Alert generation flow
- Triage and validation process
- Investigation steps
- Escalation criteria
- Containment procedures
- Communication and reporting model
- Post-incident review actions
The workflow reflects a real-world SOC operational model, demonstrating how alerts move from detection to resolution in a controlled and documented manner.
The evaluation of the organization’s security posture is documented here:
View Security Maturity Assessment
This document includes:
- Current monitoring capability evaluation
- Detection coverage analysis
- Logging and visibility gaps
- Incident response readiness assessment
- SOC process maturity level
- Recommended improvements for higher security maturity
The assessment provides a structured analysis of how well the simulated environment can detect, respond to, and prevent similar security incidents in the future.
The evidence/ directory contains extracted artifacts and log samples used during the investigation process. These files represent the raw and processed data that support the findings documented in the project.
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suspicious_queries.txt
Contains API requests identified as abnormal, including wildcard queries and cross-factory access attempts. -
high_activity_ip.txt
Lists IP addresses that generated unusually high request volumes within short timeframes. -
log_sample.txt
A filtered sample of Apache access logs used during analysis and detection logic development.
The evidence folder serves as forensic support material for:
- Behavioral analysis validation
- Detection rule creation
- Incident documentation reference
- Audit traceability
Separating evidence from documentation ensures clear investigation workflow, structured reporting, and professional project organization aligned with SOC standards.
Detailed IP activity analysis is available here:
View High Activity IP Evidence
The high_activity_ip.txt file contains IP addresses that generated an unusually high number of API requests within a short time window.
This file was created to:
- Identify potential automated activity
- Detect reconnaissance patterns
- Highlight abnormal request frequency
- Support threshold-based detection logic
High request volume from a single IP — especially immediately after authentication — can indicate:
- Enumeration attempts
- Scripted data extraction
- Misuse of legitimate credentials
- Potential insider reconnaissance
The identified IP addresses were cross-referenced with:
- Authentication logs
- Query patterns
- Accessed resources
- Time-based request clustering
This evidence directly supports the suspicious behavior detection logic documented in the Detection Logic and Incident Report sections.
A filtered Apache log sample used during investigation is available here:
The log_sample.txt file contains selected Apache access log entries extracted from the original dataset for focused analysis.
The sample highlights:
- Successful authentication events
- API request patterns
- Wildcard query usage
- Sequential machine access attempts
- High-frequency request bursts
This file was used to:
- Break down log structure and fields
- Identify abnormal behavioral indicators
- Validate detection logic conditions
- Correlate timestamps and request sequences
- Support incident timeline reconstruction
The log sample serves as primary forensic evidence supporting:
- Suspicious activity identification
- Behavioral anomaly detection
- Incident classification
- MITRE ATT&CK technique mapping
Maintaining a structured log sample improves reproducibility, audit traceability, and professional SOC documentation standards.
This project demonstrates practical, industry-aligned Security Operations Center (SOC) capabilities through structured investigation and documentation.
- Apache access log analysis
- Behavioral anomaly detection
- Suspicious API pattern identification
- Detection rule development (SIEM-oriented logic)
- Incident classification and severity assessment
- Risk evaluation and impact analysis
- MITRE ATT&CK technique mapping
- Alert triage and response workflow design
- Pattern recognition in authenticated activity
- Differentiating normal vs abnormal user behavior
- Timeline reconstruction
- Evidence correlation and validation
- Root cause assessment
- Structured incident documentation
- Enterprise-level reporting standards
- Risk-based decision making
- Security maturity evaluation
- Detection improvement recommendations
- SOC investigation methodology
- Blue Team operational mindset
- Threat-informed defense approach
- Clear technical documentation
- Structured project organization
This project reflects real-world SOC responsibilities and demonstrates readiness for entry-level security analyst or blue team roles.
The investigation identified multiple behavioral indicators consistent with reconnaissance or misuse of legitimate access.
The authenticated user executed API requests using broad wildcard parameters (e.g., factory=, machine=), enabling large-scale data visibility beyond normal operational requirements.
This behavior increases exposure of internal infrastructure details.
The same user accessed multiple factory environments within a short timeframe. Such activity deviates from typical role-based access patterns and suggests enumeration behavior.
A high volume of API calls was observed immediately after login. Sequential machine access patterns indicate possible scripted or automated enumeration.
No authentication failures or brute-force attempts were detected. The activity occurred after successful login, highlighting the risk of:
- Insider threat
- Credential misuse
- Compromised internal account
The environment lacked:
- Rate limiting controls
- Behavioral anomaly detection
- Access pattern monitoring alerts
This gap increases the risk of unnoticed reconnaissance within trusted user sessions.
While no confirmed data exfiltration or system disruption occurred, the observed behavior significantly increases the likelihood of information exposure and internal reconnaissance.
The incident demonstrates the importance of monitoring authenticated activity, not just authentication attempts.
Based on the investigation findings and security maturity assessment, the following improvements are recommended to enhance detection capability and reduce internal risk exposure.
Deploy anomaly detection mechanisms to identify abnormal authenticated activity such as:
- Wildcard API queries
- Cross-environment access patterns
- Sequential machine enumeration
- Rapid post-login request bursts
Introduce API rate limiting to prevent:
- Automated enumeration
- High-frequency scripted requests
- Large-scale data scraping
Restrict users to:
- Assigned factory environments only
- Minimum required machine visibility
- Approved operational query patterns
Apply the principle of least privilege across all roles.
Improve logging granularity by:
- Capturing detailed API parameters
- Logging query payload behavior
- Tracking user activity baselines
Configure real-time alerts for suspicious patterns.
Develop and deploy detection rules for:
- Wildcard parameter usage
- High request frequency thresholds
- Multi-factory access anomalies
- Suspicious query clustering
Assign dynamic risk scores to authenticated sessions based on:
- Access behavior
- Data volume accessed
- Query diversity
- Activity timing patterns
Perform:
- Periodic log audits
- Access entitlement reviews
- Detection coverage validation
- SOC tabletop exercises
These improvements aim to transition the environment from reactive monitoring to proactive, behavior-based threat detection aligned with modern SOC best practices.
This project provides hands-on experience with real-world security operations methodologies and strengthens practical cybersecurity skills through structured investigation and documentation.
- Understanding how web application logs are generated and analyzed
- Identifying abnormal API behavior through pattern recognition
- Developing detection logic based on behavioral indicators
- Mapping observed activity to MITRE ATT&CK techniques
- Performing risk assessment using structured evaluation models
- Log extraction and filtering
- Evidence collection and preservation
- Incident documentation using professional SOC formats
- Threat classification and severity analysis
- Detection rule development for suspicious activity
- Creating response workflows for incident handling
- Differentiating normal authenticated behavior from malicious intent
- Identifying reconnaissance patterns
- Detecting enumeration attempts
- Evaluating impact based on data sensitivity
- Performing root cause analysis
- Understanding SOC monitoring processes
- Applying least privilege principles
- Evaluating API security weaknesses
- Designing mitigation strategies for enterprise systems
This project enhances readiness for roles such as:
- SOC Analyst
- Security Analyst
- Blue Team Analyst
- Threat Detection Engineer
It demonstrates practical capability in end-to-end security incident investigation and structured reporting.
The Advanced SOC Simulation project demonstrates a complete security investigation lifecycle within a controlled environment.
Through structured log analysis, detection engineering, incident documentation, risk assessment, and MITRE ATT&CK mapping, the project highlights how suspicious authenticated behavior can be identified and mitigated.
The investigation revealed that even legitimate authentication does not guarantee safe system usage. Wildcard API queries, cross-factory access, and sequential machine enumeration present significant risks when monitoring controls are insufficient.
Key takeaways from this simulation include:
- Behavioral monitoring is critical for detecting insider threats
- Authentication must be complemented with activity-based anomaly detection
- API security requires rate limiting and strict access control enforcement
- Structured evidence collection improves forensic accuracy
- Formal incident documentation strengthens operational transparency
Although no data modification or confirmed breach occurred, the observed activity represents high-risk reconnaissance behavior that could lead to information exposure if left unmonitored.
Overall, this project reflects enterprise-level SOC practices and demonstrates practical skills in threat detection, security analysis, and incident response documentation.