Sequencer Level Security

Abstract: Current blockchains do not provide any security guarantees to the smart contracts and their users as far as the content of the transactions is concerned. In the spirit of decentralization and censorship resistance, they follow the paradigm of including valid transactions in blocks without any further scrutiny. Rollups are a special kind of blockchains whose primary purpose is to scale the transaction throughput. Many of the existing rollups operate through a centrally operated sequencing protocol. In this paper, we introduce the Sequencer Level Security (SLS) protocol, an enhancement to sequencing protocols of rollups. This pioneering contribution explores the concept of the sequencer's capability to identify and temporarily quarantine malicious transactions instead of including them in blocks immediately. We describe the mechanics of the protocol for both the transactions submitted to the rollup mempool, as well as transactions originating from Layer one. We comment on topics such as trust and decentralization, and consider the security impact on the protocol itself. We implement a prototype of the SLS protocol, Zircuit, which is built on top of Geth and the OP stack. The SLS protocol described can be easily generalized to other rollup designs, and can be used for purposes other than security.

• The paper presents the HPSAD algorithm that combines parallel and sequential anomaly detection for blockchain transactions.
• It leverages dependency analysis using read/write sets to identify interactions between transactions and optimize simulation steps.
• The approach reduces computational overhead and enhances detection accuracy by adapting processing based on transaction dependencies.

Anomaly Transaction Detection Algorithm

Anomaly detection for blockchain transactions can be optimized by combining parallel and sequential simulations. In scenarios where transactions can either be analyzed at the tip of the chain or within their specific block context, leveraging both methodologies can maximize speed while ensuring accuracy.

Algorithm Overview

1. Algorithm Name: Hybrid Parallel-Sequential Anomaly Detection (HPSAD)
2. Algorithm Description:

The algorithm described here aims to detect malicious or anomalous behavior within blockchain transactions, considering dependencies among them for efficient processing. The steps blend independent parallel processing with careful sequential analysis to yield a robust detection mechanism.

Algorithm Steps

Step 0: Transaction Selection

• Assume the sequencer has identified a list of transactions, $T_1, T_2, \ldots, T_n$, for potential inclusion in the next block.

Step 1: Parallel Simulation on the Tip of the Chain

• Execute each transaction independently using the current blockchain state at the tip of the chain.
• Perform simulations in parallel to improve speed.
• Gather results:
  1. Simulation results for each transaction.
  2. States read ($R_i$) by each transaction $T_i$.
  3. States written ($W_i$) by each transaction $T_i$.

Step 2: Transaction Dependency Analysis

• For every pair of transactions $(T_i, T_j)$, with $i < j$, determine if $T_j$ is dependent on $T_i$.
• A transaction $T_j$ is "dependent" on $T_i$ if $W_i \cap R_j \neq \emptyset$.
• Record these dependencies for further analysis.

Step 3: Parallel Detection for Independent Transactions

• Evaluate transactions identified as independent (no earlier dependencies based on sequence) in parallel.
• Use simulation results and dependency data to flag potential anomalies.

Step 4: Sequential Detection for Dependent Transactions

• For transactions identified as dependent, proceed with sequential simulation.
• Adjust the blockchain state incrementally based on the sequence of transactions.
• Each transaction, in order, considers the state changes introduced by previous transactions.

Step 5: Final Malice Detection

• Identify and flag transactions as anomalous based on simulations and dependency checks.
• Consider anomalies detected during independent and dependent transaction checks.
• Report malicious transactions to the network for appropriate action.

Conclusion

By utilizing both parallel processing for independent transaction identification and sequential processing for dependent transactions, the Hybrid Parallel-Sequential Anomaly Detection (HPSAD) algorithm efficiently reduces computational overhead while maintaining detection accuracy. This dual approach ensures quick identification of potential threats, thereby maintaining transaction security and integrity.