I build practical, affordable security systems in the cloud. My recent work: an AI‑Augmented SOC (AISOC) on AWS with ELK telemetry, a reverse‑shell simulation, and ML‑driven triage.
Lab‑scale SOC on AWS. Three EC2s (Attacker/Defender/Monitoring), reverse‑shell simulation with Metasploit, Filebeat → Elasticsearch/Kibana. ML models (TF‑IDF + Logistic Regression, Random Forest) with calibrated fusion for triage.
Containerized microservice with IaC & automation: build with Docker, deploy to a managed K8s cluster via Terraform, configure with Ansible, secure with basic policies & image scanning.
Investigations on NTFS, ext2 and FAT images using Sleuth Kit (fls, istat, blkcat), timeline building, and report writing.
Local IDS rules and alerting on a Mac M1. Custom rules, traffic capture, and tuning for noise reduction.
AWS (EC2, S3, CloudTrail) · Azure (AZ‑104) · ELK Stack (Elasticsearch, Logstash, Kibana) · Filebeat · IAM · IDS/IPS · Python · Bash · Git
Docker · Kubernetes · Terraform · Ansible · GitHub Actions
MEng, Cybersecurity — University of Limerick (2024–2025) · QCA ~3.52
BEng, Electrical & Electronics — Covenant University (2017–2022)
I enjoy building security systems that are explainable and cost‑aware. My thesis project proves that simple, calibrated models can help a small SOC punch above its weight. I like hands‑on demos, clean dashboards, and short feedback loops.
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