Cybersecurity Portfolio | Wazuh & ML Detection

Skills

Hard Skills

SIEM (Wazuh): deployment, agent management, rules/decoders, dashboards
IDS/IPS: detection tuning, false-positive reduction, threat pattern analysis
Security testing (web): functional & security testing on production websites
Detection use cases: brute force, port scanning, denial-of-service (DoS)
Machine Learning for security: Random Forest for intrusion/anomaly detection
Python: log preprocessing, automation, feature engineering
Linux (Ubuntu/Debian): system hardening, scripting, log analysis
Network monitoring: authentication, network, and system log correlation
Incident analysis: alert validation and root cause identification

Soft Skills

Critical thinking: analyzing alerts, identifying root causes, validating detections
Problem solving: resolving security issues and improving detection logic
Analytical triage: prioritizing alerts and turning noise into actionable insights
Attention to detail: validating findings during log analysis and website testing
Documentation & runbooks: creating clear steps for incident handling
Collaboration: working with security and engineering teams
Technical communication: explaining findings and detection logic clearly
Continuous learning: staying current on SIEM, IDS/IPS, and security practices

Projects

CodeBlaster AI — AI-Powered Code Review & Analysis

TypeScript · Node.js

Multi-model code review tool that combines GPT-4, Claude, and CodeLlama voting to reduce false positives while surfacing bugs, vulnerabilities, and code smells.

Features: consensus-based findings, bug & security scan (CVE), code metrics, and 20+ language coverage.
Outputs: severity-tagged issues, remediation tips, and quality scores via CLI or web dashboard.
Workflow: ./quickstart.sh → ./start interactive menu or node dist/cli.js review src/ --consensus.
Integrations: Git hooks, GitHub Actions, CI/CD runners for automated checks.

SIEM + ML Anomaly Detection

Wazuh · ML

Integrated Random Forest into Wazuh SIEM to catch network anomalies in real time, complementing rule-based detection with data-driven scoring.

Focus: brute force, port scanning, DoS.
Tech: Wazuh, Python, Random Forest, Linux.
Flow: rules/decoders → feature pipeline → RF scoring → alert thresholding.
Outcome: higher precision on priority alerts with ML-backed validation.

SentinelAI — AI Threat Detection Platform

ML · MITRE ATT&CK

ML-driven threat detection platform with real-time dashboards and MITRE ATT&CK mapping for investigation.

Detects: SQLi, XSS, brute force, port scan, DDoS, malware.
Tech: FastAPI, Isolation Forest, WebSocket, React.
Outcome: interactive view of alerts mapped to ATT&CK for faster triage.

Offensive Security Modular Framework

OffSec

Modular penetration testing framework covering recon, vuln scanning, exploitation, post-exploitation, and reporting.

Scope: web security, WAF bypass, post-exploitation.
Tech: Python, custom modules, reporting engine.
Outcome: end-to-end test flow with reusable modules and professional reports.

Web Security Testing (Production)

Security Testing

Manual and tool-assisted testing on production websites to surface exploitable issues and remediation steps.

Focus: input validation, authentication, attack surface review.
Role: security tester providing prioritized fixes.

Experience

2025

Cyber Security Intern — Sisinfo, Lembaga Ketahanan Republik Indonesia

Implemented Wazuh SIEM for multi-endpoint monitoring and integrated Random Forest to detect cyber attack anomalies.

Implemented Wazuh SIEM for centralized security monitoring.
Analyzed and processed security logs as research data for anomaly detection.
Integrated Machine Learning (Random Forest) into SIEM for anomaly detection.
Evaluated detection performance using accuracy, precision, recall, and F1-score.
Strengthened cyber defense with a hybrid rule-based + ML approach.

2024

IT Intern — Sisinfo, Lembaga Ketahanan Republik Indonesia

Focused on Linux servers and hardening while supporting production sites.

Assisted in Linux-based server management and website maintenance.
Performed routine backup, restoration, and data migration for availability.
Supported server hardening: firewall config, system updates, access control.

2020–2021

Service Center — Universitas Muhammadiyah Purwokerto

Provided hands-on technical support; built baseline troubleshooting skills.

Hardware troubleshooting and OS installation.
Developed foundational system troubleshooting and problem solving.

Education

Bachelor of Informatics — Telkom University Purwokerto

Focus: Cyber Security, SIEM, and Machine Learning.

Certifications & Training

Google Cybersecurity Professional Certificate — Coursera (Google), 2025

Comprehensive security foundations: threat detection, incident response, SIEM fundamentals, and hands-on labs.

Preview

Cyber Security Course Level Basic — ITBOX

Entry-level cyber security course covering core concepts and practical defensive techniques.

Preview

Research

Implementation of Wazuh in SIEM with Machine Learning (Random Forest) for Anomaly Detection

Undergraduate Thesis · Applied Cybersecurity Research.

Researched anomaly detection by integrating Machine Learning into Wazuh-based SIEM.
Designed a hybrid detection approach combining rule-based SIEM and Random Forest.
Built a log-based detection pipeline for brute force, port scanning, and DoS attacks.
Evaluated system performance using accuracy, precision, recall, and F1-score.

Contact

Open to opportunities in cybersecurity engineering, detection system development, and SIEM with Machine Learning. Please reach out via email, LinkedIn, or GitHub.

Email LinkedIn GitHub