Machine Learning & Optimization
Efficient learning methods, neural networks, and metaheuristic optimization.
Research Profile
Machine Learning & IoT Security for Smart Infrastructure
I am an M.Sc. graduate in Information Technology Engineering with a B.Sc. in Civil Engineering, working at the intersection of machine learning, IoT security, and smart infrastructure.
My research focuses on lightweight intrusion and anomaly detection for IoT and cyber-physical systems, with particular interest in neural networks and optimization-based methods. I am currently seeking funded PhD opportunities involving AI for smart infrastructure, digital twins, intelligent transportation, infrastructure monitoring, and secure cyber-physical systems.
Research Interests
IoT Security & Intrusion Detection
Lightweight detection for networked, resource-constrained, and cyber-physical systems.
Anomaly Detection
Reliable detection with attention to false alarms, class imbalance, and model evaluation.
AI for Smart Infrastructure
Data-driven intelligence for civil and urban infrastructure systems.
Digital Twins & Cyber-Physical Systems
Integration of sensing, analytics, secure computing, and physical assets.
Intelligent Transportation & Monitoring
Machine learning for mobility, prediction, infrastructure monitoring, and assessment.
Selected Publications
Accepted
Enhancing Intrusion Detection Using MAG: A Shallow Multilayer Perceptron Tuned with Adam and Grey Wolf Optimization
Published
Internet of Things (IoT) Intrusion Detection by Machine Learning (ML): A Review
Doctoral Opportunities
Open to Funded PhD Research Opportunities
I am interested in doctoral research at the intersection of machine learning, IoT security, smart infrastructure, digital twins, intelligent transportation, and cyber-physical systems.