Table 4.

Selection of baseline models for comparative analysis.

Analysis dimension	Baseline model	Comparative justification	Key parameters of baseline model
Multi-dimensional attack detection performance comparison	Traditional static role-based access control (RBAC) model	Evaluates performance differences between static and dynamic strategies in detecting emerging attacks19	Role-permission matrix: 100 × 50; rule update cycle: fixed at 24 h
	Q-learning algorithm	Validates SAC’s superiority over traditional RL in policy exploration and multi-objective trade-offs20	Learning rate: 0.01; discount factor: 0.99; state space dimension: node degree + link load (50 dimensions)
	ML-based detector (SVM - support vector machine)	Compares traditional feature engineering with GNN for dynamic topology-based attack detection sensitivity21	Feature dimensions: 20 (packet rate, connection count, etc.); kernel function: RBF (radial basis function); penalty factor c = 10
Dynamic resource allocation balance validation	Greedy algorithm	Highlights the contrast between global optimization and local optimal strategies in resource balancing22	Link Selection Policy: Lowest real-time load priority; History State Memory Window: None.
	Shortest path first	Validates the necessity of multi-objective optimization compared to single-objective (minimum hops)23	Routing update interval: 5 s; link cost calculation: hop count; maximum path count: 3
Robustness evaluation under topology variations	Dynamic programming graph generation (DPGG) model	Compares centralized vs. federated architectures in terms of policy stability in dynamic topologies24	Network structure: actor (256–128–64)—critic (256–128); experience replay buffer size: 1e5; batch size: 64
Distributed architecture efficiency & scalability testing	Not applicable	N/A	N/A