Blockchain Attack Vectors
Learn about different blockchain attacks and simulate them in a controlled environment.
Priority Attack Vectors
These advanced attack types represent the greatest security threats to blockchain networks and are the primary focus of this research.
BlackBird 51% Attack
Description
A BlackBird 51% attack occurs when an attacker gains control of more than 50% of the network's hash rate, allowing them to manipulate blockchain consensus and potentially rewrite transaction history.
Characteristics
- Attacker controls majority of network hash power
- Can reverse transactions and perform double-spending
- Able to prevent transaction confirmations
- Can exclude other miners from finding valid blocks
Detection Methods
- Real-time hash rate distribution monitoring
- Unusual chain reorganization detection
- Network anomaly analysis
- ML-based pattern recognition for early warnings
BlackBird 51% Attack Simulation Results
Black Bird Embedded Double-Spending Attack (BBEDSA)
Description
BBEDSA is a sophisticated variant combining elements of 51% attacks with strategic transaction manipulation. The attacker embeds conflicting transactions across multiple blocks, making detection particularly difficult.
Characteristics
- Creates shadow chains with conflicting transactions
- Embeds malicious transactions across multiple blocks
- Performs sophisticated double-spending through chain manipulation
- Exploits transaction verification weaknesses
Detection Methods
- Transaction graph analysis for conflicting patterns
- Chain reorganization monitoring
- Authentication verification for high-value transactions
- Advanced ML anomaly detection for embedded attacks
BBEDSA Attack Simulation Results
Denial of Chain (DoC) Attack
Description
A Denial of Chain attack floods the blockchain with spam transactions, causing network congestion, increased fees, and preventing legitimate transactions from being processed in a timely manner.
Characteristics
- Massive transaction spamming
- Network congestion and throughput reduction
- Transaction processing delays
- Fee market manipulation
Detection Methods
- Transaction volume anomaly detection
- Sender pattern analysis
- Transaction value profiling
- Network congestion monitoring
DoC Attack Simulation Results
Secondary Attack Vector
This attack type has been recently implemented but is not a primary focus of the security research.
Timejacking Attack
Description
A Timejacking attack manipulates a node's network time perception to trick it into accepting an alternative blockchain or rejecting valid blocks, effectively isolating the node from the legitimate network.
Characteristics
- Manipulates timestamping mechanisms
- Exploits time-based validation rules
- Creates artificial time gaps between blocks
- Can affect block acceptance decisions
Detection Methods
- Timestamp consistency verification
- Network time protocol synchronization checks
- Block timing anomaly detection
- Multiple time source validation
Timejacking Attack Simulation Results
Additional Attack Vectors
These traditional attack types are included for completeness but are not the primary focus of our security research.
Sybil Attack
Description
A Sybil attack occurs when a malicious actor creates multiple identities (nodes) to gain a disproportionate influence over the network. This can lead to consensus manipulation and double-spending.
Characteristics
- Multiple nodes created by the same entity
- Nodes often exhibit similar behavior patterns
- May control a significant portion of network hash power
- Can potentially influence transaction validation
Detection Methods
- Network analysis to identify node clusters
- Hash rate monitoring for abnormal distribution
- IP address correlation
- Machine learning-based pattern recognition
Sybil Attack Simulation Results
Eclipse Attack
Description
An Eclipse attack occurs when an attacker takes control of all connections to and from a specific node, effectively isolating it from the legitimate network. This allows the attacker to feed false information to the victim node.
Characteristics
- Target node is isolated from legitimate peers
- Attacker controls all information flow to the victim
- Victim may receive a manipulated view of the blockchain
- Can lead to double-spending and consensus issues
Detection Methods
- Network topology monitoring
- Connection diversity checks
- Peer address verification
- Anomaly detection in block propagation times
Eclipse Attack Simulation Results
Finney Attack
Description
The Finney attack is a sophisticated double-spending technique where an attacker pre-mines a block containing a transaction back to themselves, then makes a purchase and broadcasts the pre-mined block to reverse the transaction.
Characteristics
- Requires significant mining power
- Relies on zero-confirmation transactions
- Targets merchants who accept fast payments
- Attacker must have a pre-mined block ready
Detection Methods
- Requiring multiple confirmations
- Monitoring for conflicting transactions
- Tracking orphaned blocks
- Analyzing network propagation patterns