AI-driven trust-centric decision framework for cybersecurity intrusion detection systems.
This project explores trust-based AI decision systems for intrusion detection using machine learning models, trust scoring mechanisms, and cybersecurity evaluation pipelines.
The framework focuses on improving reliability, adaptability, and decision quality across cybersecurity datasets using trust-oriented evaluation methods.
- Intrusion Detection System (IDS) pipelines
- Trust-based decision mechanisms
- Machine learning model evaluation
- Cybersecurity dataset processing
- Adversarial and poisoning evaluation
- Multi-model comparison workflows
- AI-driven trust scoring concepts
- NSL-KDD
- UNSW-NB15
- CICIDS2017
- Python
- Scikit-learn
- Pandas
- NumPy
- Machine Learning Pipelines
- Artificial Intelligence
- Cybersecurity
- Trust-Based Systems
- Intrusion Detection
- Adversarial Robustness
- Intelligent Decision Frameworks