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Detecting threats in a Hyperledger Fabric multi-party computation system

You work for an organization that uses multi-party computation systems. Blockchain infrastructure incorporates a diverse set of technologies, across a distributed network, which means it faces some unique challenges. Some of the most significant infrastructure threats include denial-of-service (DoS), key theft, network partitioning, consensus manipulation, and blockchain integrity attacks. You need to be able to consistently detect and handle these threats. 

Data required

Hyperledger Fabric

Procedure

You can analyze Hyperledger Fabric’s data with the following tools:

Your organization can use Hyperledger Fabric as a way to collaborate without compromising on safety and integrity. The Security Monitoring dashboard in the app provides access to a high level view of several threat indicators:

  • Indicators of DoS include trends in transaction latency and throughput, unique senders, and open gRPC connections.
  • Orphaned blocks may indicate attacks on blockchain integrity.
  • Indicators of consensus manipulation include configuration updates and consensus leader changes.

You can expand on the searches in any of these dashboards yourself, using events captured from data utilized by blockchain environments, including node logs or ledger data, and Prometheus/StatsD Hyperledger Fabric metrics. This data can be separated into on-chain and off-chain data.

  • On-chain data consists of the ledger data, which is persistent and resistant to tampering from network-wide replication.
  • Off-chain data is often ephemeral and may not be widely distributed. Examples of off-chain data include the state database, network traffic, as well as node metrics and logs.
Threat Indicators Data Sources

Denial-of-service

Tx throughput & latency

Block latency

Number of Senders

Number of Open connections

Block headers

Node metrics

Consensus manipulation

Changes in chain configuration

Leadership Elections

Blocks

Node logs

Ledger manipulation

Orphaned blocks

Block headers

Example: Denial-of-service attack

In this scenario, an authorized user has their keys compromised and begins spamming the network with transactions.

In a normal situation where a single client sends 10 transactions per second, your dashboard might look like the following image. Note the transaction latency, throughput, and number of open connections.

In this example DoS attack, a single client opens persistent 1000 connections, each performing 1 query per minute. The transaction latency starts to increase, transactions per second decreases, and the number of open connections increases. At this point, it may be difficult to determine if this change is because of a high period of load, misconfiguration, or a denial of service attack.

You can investigate further in the Infrastructure Health and Monitoring Dashboard, where you can see connection and I/O timeout errors.

Because you have noticed a large number of open connections, you should query Splunk to see the distribution of gRPC message subjects and addresses.

index=hyperledger_logs "line.grpc.method"!=""
| stats count BY line.grpc.peer_subject
| rename line.grpc.peer_subject AS Subject, count AS Count

When you perform this search you may see a large discrepancy in message count. In this example, the screenshot below shows that “User1@buttercup.example.com,L=San Francisco,ST=California,C=US” is likely compromised or misconfigured and should be investigated further.

Next steps

The content in this guide comes from a previously published blog, one of the thousands of Splunk resources available to help users succeed. In addition, these Splunk resources might help you understand and implement this use case:

Need technical help? Explore our customer success resources to find education and training, engage experts through OnDemand services, view support options, and more.