Self-Healing AI-Driven Networks for Automated Cyber Threat Detection and Recovery
Introduces an AI-assisted framework for autonomous detection, diagnosis, and recovery across distributed cloud-edge environments.
Professional Summary
Joshua Seyi Ibitoye is a cloud security, DevSecOps, and AI systems engineer focused on secure infrastructure, intelligent automation, and applied cybersecurity research. His work connects cloud architecture, digital forensics, network reliability, and machine learning to build systems that can detect, recover, and improve with less manual intervention.
Education
M.S. Cybersecurity
Southeast Missouri State University
M.S. Networking and Information Security
University of Ibadan
B.Tech. Computer Science and Engineering
Ladoke Akintola University of Technology
Focus Areas
Cloud security architecture
DevSecOps and infrastructure automation
AI-assisted threat detection
Digital forensics and incident response
Self-healing networks
Secure systems integration
What I Build
Featured Publications
Modeling Threat Vectors in Real Time Using AI-Enhanced Surveillance Analytics Across Cyber, Land, Air, and Maritime Domains
Proposes a unified AI framework for modeling and detecting threat vectors across cyber, land, air, and maritime environments.
Multi-Agent AI Systems for Secure, Transparent, and Compliant Fraud Surveillance in Cross-Border FinTech Operations
Introduces a multi-agent AI framework for real-time fraud detection, transparency, and compliance in cross-border FinTech systems.
Zero-Trust Cloud Security Architectures with AI-Orchestrated Policy Enforcement for U.S. Critical Sectors
Presents an AI-orchestrated zero-trust framework for securing cloud infrastructures in critical sectors.
AI-Driven Privacy-Preserving Contact Tracing and Pandemic Response Systems
Proposes a federated learning and zero-trust architecture for privacy-preserving COVID-19 contact tracing and pandemic response.
Selected Projects
Systems and research prototypes across cloud security, fraud detection, remote access, and automated recovery.
Self-Healing Network Prototype
AI-assisted detection, diagnosis, and recovery workflow for cyber-resilient network operations.
AI Credit Card Fraud Detection
Machine-learning pipeline for identifying fraudulent transactions in highly imbalanced financial datasets.
Cloud Security Landing Zone
AWS foundation for secure workloads, identity boundaries, CI/CD automation, and repeatable infrastructure delivery.
High-Performance Remote Desktop Access
Secure remote access design for reliable operations across distributed technical teams and support environments.
Zero-Trust Policy Automation
AI-assisted policy orchestration pattern for adaptive access control and cloud workload containment.
Talks and Presentations
Credit Card Fraud Detection Using Machine Learning: An AI-Driven Approach for Financial Security
Presentation of an AI-based credit card fraud detection system using supervised learning, class-imbalance correction, and model comparison for financial security use cases.