Security Detection · Network Engineering · Automation
Polytechnic University of the Philippines · GPA: 1.23 / 1.00
I build detection systems and network infrastructure that hold under real conditions, not just controlled labs.
My work focuses on:
- Translating ambiguous security problems into repeatable detection logic
- Designing networks with enforced boundaries and validated failover
- Automating infrastructure with state verification, not just deployment
Most student projects stop at “it works.” Mine are built to answer:
Does it still work under failure, scale, and misconfiguration?
- Build SIEM-style detection logic from raw logs (not dashboards on clean data)
- Design segmented enterprise networks with measurable resilience
- Implement network-as-code pipelines with drift detection
- Develop risk-based anomaly detection systems using SQL correlation logic
Repo: https://github.com/janinelurenana/cloud-security-analyzer
Built a modular detection pipeline that processes CloudTrail-style logs and produces severity-ranked, MITRE ATT&CK–mapped findings.
- 8 detection rules across 26 simulated cloud resources
- 43 validated findings surfaced via Streamlit
- Config-driven thresholds (no hardcoded detection logic)
What matters:
- Detection logic is decoupled from thresholds → mirrors real SIEM rule design
- Outputs are explicitly mapped to attacker behavior, not generic alerts
Mapped Techniques:
- T1078 — Valid Accounts / Privilege Abuse
- T1098 — Account Manipulation
- T1110 — Brute Force
Repo: https://github.com/janinelurenana/Enterprise-Campus-Network-Architecture
Designed and validated a segmented campus network with enforced least privilege and high availability.
- VLAN segmentation: IT, HR, Guest, DMZ
- ACL-enforced zone isolation (not theoretical)
- FortiGate Active–Passive HA
Validation, not assumption:
- ~1 second failover convergence
- Session persistence during link failure
Additional work:
- Documented HSRP convergence behavior (5–10s)
- Extending to multi-site OSPF + IPsec VPN
Repo: https://github.com/janinelurenana/declarative-network-provisioning
Built a multi-vendor network-as-code pipeline:
YAML → Jinja2 → Netmiko → Device Configuration → State Verification
Key capability:
-
Drift detection is bidirectional
- Detects missing intended config
- Detects unauthorized changes
Why this matters: Most automation scripts push configs. This enforces state integrity, which is what production environments actually need.
Repo: https://github.com/janinelurenana/banking-fraud-detection-analytics
Built a transaction analysis system using weighted anomaly scoring, not simple thresholds.
- 5,500 transactions across 200 accounts
- Multi-table joins with CASE-based scoring
Detection includes:
- Velocity anomalies
- Geographic inconsistencies
- High-value outliers
Key insight: Single signals don’t trigger alerts—combined risk does → aligns with real SIEM correlation logic
Output:
- Star schema → Power BI dashboards
- Geographic threat clustering for interpretability
Security
- Log Analysis · MITRE ATT&CK
- IAM Auditing · CSPM
- Burp Suite (SQLi) · Nmap
Networking
- GNS3 · FortiGate HA
- OSPF · VLANs · ACLs
- IPsec VPN · HSRP / VRRP
- NAT · STP
Programming / Automation
- Python · SQL
- Jinja2 · YAML
- Pandas · Streamlit · Netmiko
Tooling
- Power BI · MySQL
- AWS CloudTrail
- Kali Linux
- Git / GitHub
- Multi-site network simulation (OSPF + IPsec WAN)
- Detection engineering aligned with SOC workflows via TryHackMe
- CompTIA Security+ (target: Q3 2026)
Open to junior-level roles, not just internships:
- Security Analyst
- Network Engineer
- Detection Engineer (entry/junior)