# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 3 Related Work
### Consensus Protocol Vulnerabilities  
Several studies have explored the DoS resilience of consensus protocols. [PERSON] et al. [39] simulated static-leader attack and leader-tracking attack and examined leader-based consensus resilience. Their study demonstrated that low-bandwidth attacks, as small as tens of Mbps, can halt the HotStuff consensus due to its reliance on a single leader. However, the experiment was based on standalone implementations rather than an actual blockchain platform, so the impact on HLF's performance requires further investigation. A comparative study [40] benchmarked PBFT, Raft, and PoET under DDoS conditions in Hyperledger Sawtooth, and found that Raft was the most vulnerable consensus among them, as it exhibited the highest CPU utilization, response time degradation, and packet loss in the attack. However, since this study focused exclusively on Sawtooth and IoT applications, the impact on HLF remains unverified. [PERSON] et al. [41] analyzed Raft's performance on both HLF and Ethereum in flood DoS scenarios, demonstrating that Raft is susceptible to Byzantine faults. To mitigate DoS vulnerabilities associated with static and explicit consensus committees, [PERSON] et al. [42] proposed a consensus protocol named EGES. It leverages Intel SGX to implement a stealth committee mechanism and obfuscate validator identities to enhance security. Their experiment compared EGES to nine other baseline protocols and showed that EGES provides robustness, scalability, and resistance to  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline Study & Methodology & Main Findings \\ \hline  & \multicolumn{2}{c}{Application Layer Vulnerabilities} \\ \hline [PERSON] et al. (2024) [47] & Controlled simulations of MTC attack. & Showed that persistent logical conflicts prevent transaction validation; proposed redefining conflict rules for mitigation. \\ \hline  & \multicolumn{2}{c}{Detection and Defense Mechanisms} \\ \hline [PERSON] et al. (2022) [48] & Proposed CNN-based detection (CMCNN). & CMCNN outperformed traditional models for network-layer DoS detection in blockchain systems. \\ [PERSON] et al. (2024) [49] & Applied GRU-based sequential model. & GRU achieved high accuracy and robustness in detecting DDoS within Fabric. \\ [PERSON] et al. (2025) [50] & Static analysis using SSA form for chaincode. & Detected DoS-prone Read/Write logic flaws in Go chaincode pre-deployment. \\ [PERSON] et al. (2024) [51] & AMRA: SDN-based monitoring framework. & Proposed AMRA framework was effective in identifying anomalous traffic patterns including DoS behavior. \\ [PERSON] and [PERSON] (2020) & Log aggregation with rule-based threat detection. & Introduced indicators for security threats; developed a threat-monitoring framework aggregating logs from blockchain components. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
DoS attacks. While EGES demonstrated promising results in Ethereum, its applicability and performance in the HLF context needs to be explored.  
These findings collectively underscore the susceptibility of consensus protocols to DoS attacks, yet there is a lack of focused empirical evaluation within real HLF deployments, a gap we directly address. Our choice of Raft protocols was motivated by its prominence as HLF's default consensus and its demonstrated weaknesses under adversarial conditions.