Self-healing AI-driven networks for Automated Cyber. . .
Introduces a multi-agent AI framework for autonomous detection, diagnosis, and recovery of cyber incidents across distributed cloud-edge environments.
Professional Summary
Joshua Seyi Ibitoye is a Cloud & Security Engineer and AI-driven developer with extensive experience in network security, digital forensics, and DevSecOps automation. He has engineered scalable infrastructures, optimized AI model evaluation systems, and led secure integrations for global clients. Joshua is passionate about building intelligent, self-healing systems that unite cybersecurity networking, and artificial intelligence.
Education
M.S. Cybersecurity
Southeast Missouri State University
M.S. Networking & Information Security
University of Ibadan
B.Tech. Computer Science & Engineering
Ladoke Akintola University of Technology
Interests
Cloud Security & DevOps
AI for Network Reliability
Digital Forensics & Automation
Intelligent Infrastructure Design
Cloud Computing
Networking
Music 😉
📚 My Research
I research how artificial intelligence can strengthen cloud security and enable self-healing networks. My goal is to build intelligent, resilient systems that detect, prevent, and recover from cyber threats automatically.
I share insights on AI security, DevSecOps, and autonomous cloud infrastructure.
Always open to collaboration on innovative cloud & security projects. 🤝
Featured Publications
Modeling Threat Vectors in Real-Time Using AI-Enhanced Surveillance Analytics . . .
Proposes a unified AI framework for modeling and detecting threat vectors across cyber, land, air, and maritime environments in real time.
Multi-Agent AI Systems for Secure, Transparent, and Compliant Fraud. . .
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. . .
Presents an AI-orchestrated Zero-Trust framework for securing cloud infrastructures in critical sectors of the U.S. economy.
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.
Recent Publications
Recent & Upcoming Talks
Credit Card Fraud Detection Using Machine Learning: An AI-Driven Approach for Financial Security
Presentation of an AI-based credit card fraud detection system integrating supervised learning and SMOTE for class imbalance correction. Comparison of ensemble and neural models …
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