Page 260 - 《软件学报》2025年第7期

张孝 等: 区块链测试基准综述 3181

Int’l Conf. on Computer Systems and Applications (AICCSA). Antalya: IEEE, 2020. 1–8. [doi: 10.1109/AICCSA50499.2020.9316536]
[46] Dong ZL, Zheng EM, Choon Y, Zomaya AY. DAGBENCH: A performance evaluation framework for DAG distributed ledgers. In:
Proc. of the 12th IEEE Int’l Conf. on Cloud Computing (CLOUD). Milan: IEEE, 2019. 264–271. [doi: 10.1109/CLOUD.2019.00053]
[47] Wang R, Ye KJ, Wang Y, Xu CZ. xBCBench: A benchmarking tool for analyzing the performance of blockchain systems. In: Proc. of
the 3rd Int’l Conf. on Blockchain and Trustworthy Systems. Guangzhou: Springer, 2021. 101–114. [doi: 10.1007/978-981-16-7993-3_8]
[48] Alom I, Ferdous MS, Chowdhury MJM. BlockMeter: An application agnostic performance measurement framework for private
blockchain platforms. arXiv:2202.05629, 2022.
[49] Ren ZJ, Xiang HT, Zhou ZH, Wang N, Jin HQ. AlphaBlock: An evaluation framework for blockchain consensus algorithms. In: Proc. of
the 9th Int’l Workshop on Security in Blockchain and Cloud Computing. ACM, 2021. 17–22. [doi: 10.1145/3457977.3460297]
[50] Klenik A, Kocsis I. Porting a benchmark with a classic workload to blockchain: TPC-C on Hyperledger Fabric. In: Proc. of the 37th
ACM/SIGAPP Symp. on Applied Computing. ACM, 2022. 290–298. [doi: 10.1145/3477314.3507006]
[51] Zheng PL, Zheng ZB, Luo XP, Chen XP, Liu XZ. A detailed and real-time performance monitoring framework for blockchain systems.
In: Proc. of the 40th IEEE/ACM Int’l Conf. on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP).
Gothenburg: IEEE, 2018. 134–143.
[52] Touloupou M, Themistocleous M, Iosif E, Christodoulou K. A systematic literature review toward a blockchain benchmarking
framework. IEEE Access, 2022, 10: 70630–70644. [doi: 10.1109/ACCESS.2022.3188123]
[53] Chacko JA, Mayer R, Fekete A, Gramoli V, Jacobsen HA. How to benchmark permissioned blockchains. In: Proc. of the 15th TPC
Technology Conf. on Performance Evaluation & Benchmarking (TPCTC 2023). 2023. https://gramoli.github.io/pubs/TPCTC2023-
preprint.pdf
[54] Anderson JC, Lehnardt J, Slater N. CouchDB: The Definitive Guide: Time to Relax. Sebastopol: O’Reilly Media, 2010.
[55] Dwork C, Naor M. Pricing via processing or combatting junk mail. In: Proc. of the 12th Annual Int’l Cryptology Conf. on Advances in
Cryptology. Santa Barbara: Springer, 1993. 139–147. [doi: 10.1007/3-540-48071-4_10]
[56] Lamport L. The part-time parliament. ACM Trans. on Computer Systems, 1998, 16(2): 133–169. [doi: 10.1145/279227.279229]
[57] Ongaro D, Ousterhout J. In search of an understandable consensus algorithm. In: Proc. of the 2014 USENIX Conf. on USENIX Annual
Technical Conf. Philadelphia: USENIX Association, 2014. 305–320.
[58] Castro M, Liskov B. Practical Byzantine fault tolerance and proactive recovery. ACM Trans. on Computer Systems (TOCS), 2002,
20(4): 398–461. [doi: 10.1145/571637.571640]
[59] Szabo N. Smart contracts: Building blocks for digital markets. EXTROPY: The Journal of Transhumanist Thought, 1996, 18(2): 28.
[60] Luu L, Chu DH, Olickel H, Saxena P, Hobor A. Making smart contracts smarter. In: Proc. of the 2016 ACM SIGSAC Conf. on
Computer and Communications Security. Vienna: ACM, 2016. 254–269. [doi: 10.1145/2976749.2978309]
[61] Cheng JR, Xie LY, Tang XY, Xiong NX, Liu BY. A survey of security threats and defense on blockchain. Multimedia Tools and
Applications, 2021, 80(20): 30623–30652. [doi: 10.1007/s11042-020-09368-6]
[62] Saad M, Spaulding J, Njilla L, Kamhoua C, Shetty S, Nyang D, Mohaisen D. Exploring the attack surface of blockchain: A
comprehensive survey. IEEE Communications Surveys & Tutorials, 2020, 22(3): 1977–2008. [doi: 10.1109/COMST.2020.2975999]
[63] Huang JJ, Han SM, You W, Shi WC, Liang B, Wu JZ, Wu YJ. Hunting vulnerable smart contracts via graph embedding based bytecode
matching. IEEE Trans. on Information Forensics and Security, 2021, 16: 2144–2156. [doi: 10.1109/TIFS.2021.3050051]
[64] Zhuang Y, Liu ZG, Qian P, Liu Q, Wang X, He QM. Smart contract vulnerability detection using graph neural networks. In: Proc. of the
29th Int’l Joint Conf. on Artificial Intelligence. 2021. 3283–3290.
[65] Apostolaki M, Zohar A, Vanbever L. Hijacking Bitcoin: Routing attacks on cryptocurrencies. In: Proc. of the 2017 IEEE Symp. on
Security and Privacy (SP). San Jose: IEEE, 2017. 375–392. [doi: 10.1109/SP.2017.29]
[66] Marcus Y, Heilman E, Goldberg S. Low-resource eclipse attacks on Ethereum’s peer-to-peer network. Cryptology ePrint Archive,
2018/236, 2018.
[67] Saad M, Thai MT, Mohaisen A. POSTER: Deterring DDoS attacks on blockchain-based cryptocurrencies through mempool
optimization. In: Proc. of the 2018 Asia Conf. on Computer and Communications Security. Incheon: ACM, 2018. 809–811. [doi: 10.
1145/3196494.3201584]
[68] Li HN, Wang K, Miyazaki T, Xu CH, Guo S, Sun YF. Trust-enhanced content delivery in blockchain-based information-centric
networking. IEEE Network, 2019, 33(5): 183–189. [doi: 10.1109/MNET.2019.1800299]
[69] Swathi P, Modi C, Patel D. Preventing sybil attack in blockchain using distributed behavior monitoring of miners. In: Proc. of the 10th
Int’l Conf. on Computing, Communication and Networking Technologies (ICCCNT). Kanpur: IEEE, 2019. 1–6. [doi: 10.1109/
ICCCNT45670.2019.8944507]