Autonomous Trust and Access Control in Coalition IoBT Networks
| Title | Autonomous Trust and Access Control in Coalition IoBT Networks |
|---|---|
| Summary | Design a decentralized, behavior-based trust and access control system that adapts autonomously under disconnected, adversarial conditions. |
| Keywords | |
| TimeFrame | |
| References | Trust-based Blockchain Authorization for IoT
https://arxiv.org/pdf/2104.00832 Blockchain-based Decentralized Trust Management in IoT: Systems, Requirements and Challenges https://link.springer.com/article/10.1007/s40747-023-01058-8 |
| Prerequisites | |
| Author | |
| Supervisor | Edison Pignaton de Freitas |
| Level | Master |
| Status | Open |
Problem:
Coalition operations require dynamic trust and access control across multiple organizations. Centralized RBAC/PKI solutions fail in partitioned environments, leading to risks of insider threats and impersonation attacks.
Goal: Design a decentralized, behavior-based trust and access control system that adapts autonomously under disconnected, adversarial conditions.
Proposed Solution & Tasks: Implement local ABAC/RBAC policies enforced with ephemeral tokens. Develop behavioral trust models that score nodes based on mobility patterns, resource use, and anomaly detection. Incorporate location-validation techniques (e.g., UAVouch-style*) to detect Sybil or impersonation attacks. Test resilience in coalition networks with different trust domains.
Evaluation Criteria: Accuracy of malicious/insider node detection (precision, recall, F1-score). Policy enforcement latency and overhead. Scalability with increasing coalition size. Success rate in preventing unauthorized access during adversarial attacks.
Suggested Tools & Platforms: Simulation Frameworks: OMNeT++ with INET/Veins modules → simulate coalition networks with adversarial behaviors. NS-3 with mobility models → test trust policies with moving IoBT nodes (drones, vehicles).
Trust/Access Control Libraries: Open Policy Agent (OPA) → ABAC/RBAC policies in distributed systems. Keycloak → identity management (extend with ephemeral tokens). Machine Learning Tools (for behavioral trust scoring): Scikit-learn or TensorFlow Lite → train anomaly detectors on node behavior (bandwidth use, mobility patterns).
Datasets: UNSW-NB15 and CIC-IDS2017 → intrusion detection datasets for anomaly detection. Mobility traces: CRAWDAD datasets (e.g., military mobility traces, UAV movement). Synthetic mobility models (Random Waypoint, Gauss-Markov) in NS-3/OMNeT++.
