Attack Simulation Lab

Test the blockchain security system by simulating different types of attacks and observing the detection and mitigation responses.

Select Attack Vector

Priority Attack Vectors
BlackBird 51% Attack

Control blockchain consensus by gaining majority hash power.

BBEDSA

Advanced double-spending with transaction embedding.

Denial of Chain (DoC)

Flood the network with transactions to cause congestion.

Additional Attack Vectors
Sybil Attack

Multiple fake identities disrupt consensus.

Eclipse Attack

Isolate a node from the network.

Finney Attack

Double-spending with pre-mined blocks.

Timejacking

Manipulate node's network time perception.

Simulation Console

Ready
Initialize Network: Add legitimate nodes and transactions to the blockchain.
Launch Attack: Select an attack type to see description.
Detection Phase: ML models analyze the blockchain for suspicious patterns.
Mitigation Response: Implement defensive countermeasures.
Verification: Confirm network integrity after mitigation.
Ready to simulate blockchain attacks. Select an attack vector from the left panel to begin.

Detection & Mitigation Results

Detection Results

No detection data available yet. Run a simulation first.

Mitigation Strategy

No mitigation data available yet. Run a simulation first.

Detection & Mitigation Results

Model Performance Metrics

Performance metrics for all seven ML models based on test data evaluation

Adaptive Security Response

Current Sensitivity Level
Moderate
Response Actions by Sensitivity Level
Sensitivity Actions
Low Flag transaction, require additional confirmations
Moderate Flag transaction, require additional confirmations, delay transaction
High Flag transaction, delay transaction, strengthen node validation
Very High Reject transaction temporarily, manual verification, issue network alert
Extreme Reject transaction, isolate node, network-wide alert, emergency consensus adjustment

Ensemble Model Comparison

Ensemble Techniques
  • Voting Ensemble: Combines multiple models through a voting mechanism
  • Stacking Ensemble: Uses predictions from multiple models as input to a meta-model
  • Boosting: Sequentially builds models that correct errors from previous ones
  • Bagging: Builds multiple models on different subsets of data and averages results
Benefits of Ensemble Approach
  • Reduced overfitting and improved generalization
  • Enhanced robustness against different attack vectors
  • Improved detection accuracy across varied attack patterns
  • Better resilience against adversarial evasion techniques

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