Table 3 Attack capabilities and success probabilities in centralized versus BDEQ architecture.
From: Enhancing secure IoT data sharing through dynamic Q-learning and blockchain at the edge
Items | Capability (scenario 1) | Probability (scenario 1) | Capability (scenario 2) | Probability (scenario 2) |
|---|---|---|---|---|
Pre-transmission | Hack into n nodes | \(\mathop \prod \limits_{i = 1}^{N} \lambda_{i}\) | Hack into n DS-PSPNs | \(\left( {1 - \mathop \prod \limits_{i = 1}^{N} t_{i} } \right)\mathop \prod \limits_{i = 1}^{N} \lambda_{i}^{\prime }\) |
In-transit | Hack n channels | \(\mathop \prod \limits_{i = 1}^{N} \eta_{i}\) | Forge authorization | \(\left( {1 - \mathop \prod \limits_{i = 1}^{N} t_{i} } \right)\mathop \prod \limits_{i = 1}^{N} \varepsilon^{\prime }_{i} \times \mathop \prod \limits_{i = 1}^{N} \lambda^{\prime }_{i}\) |
Post-reception | Hack into the control center | µ | Hack into N record pool | \(\left( {1 - \mathop \prod \limits_{i = 1}^{N} t_{i} } \right)\mathop \prod \limits_{i = 1}^{N} \varepsilon^{\prime }_{i} \times \mathop \prod \limits_{i = 1}^{N} \eta^{\prime }_{i}\) |
