Research article Special Issues

A type of block withholding delay attack and the countermeasure based on type-2 fuzzy inference

  • Received: 19 August 2019 Accepted: 25 September 2019 Published: 09 October 2019
  • We proposed a new type of bitcoin withholding attack named block withholding delay (BWD). It is different from the traditional withholding attacks which always drop valid blocks. BWD attackers never discard blocks but they delay the submissions of blocks to the pool managers, resulting the pool failed in the mining competitions and loss of rewards. We analyzed the optimum strategy of a BWD attacker who split its computing power into two parts, one was utilized to launch BWD attacks on the victim pools, while the other part was used for solo mining. We present detailed quantitative analysis of the maximum incentive that an attacker can earn by carefully splitting its computing power, and demonstrated that the attacker can obtain higher incentives than its contribution to the network in different conditions. Furthermore, we proposed a countermeasure against BWD based on the interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). The principle is to modify the private payoff scheme of pools to increase the risk of losing revenues of the rogue miners who deliberately delay block submissions. The scheme dealing the uncertain cause of block delay using fuzzy inference, and it is so designed that it does not require modifications of public mining protocols or data structures of the bitcoin network, which makes it applicable in practical pools.

    Citation: Liang Liu, Wen Chen, Lei Zhang, JiaYong Liu, Jian Qin. A type of block withholding delay attack and the countermeasure based on type-2 fuzzy inference[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 309-327. doi: 10.3934/mbe.2020017

    Related Papers:

  • We proposed a new type of bitcoin withholding attack named block withholding delay (BWD). It is different from the traditional withholding attacks which always drop valid blocks. BWD attackers never discard blocks but they delay the submissions of blocks to the pool managers, resulting the pool failed in the mining competitions and loss of rewards. We analyzed the optimum strategy of a BWD attacker who split its computing power into two parts, one was utilized to launch BWD attacks on the victim pools, while the other part was used for solo mining. We present detailed quantitative analysis of the maximum incentive that an attacker can earn by carefully splitting its computing power, and demonstrated that the attacker can obtain higher incentives than its contribution to the network in different conditions. Furthermore, we proposed a countermeasure against BWD based on the interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). The principle is to modify the private payoff scheme of pools to increase the risk of losing revenues of the rogue miners who deliberately delay block submissions. The scheme dealing the uncertain cause of block delay using fuzzy inference, and it is so designed that it does not require modifications of public mining protocols or data structures of the bitcoin network, which makes it applicable in practical pools.


    加载中


    [1] S. Nakamoto, bitcoin: A peer-to-peer electronic cash systemTech, 2008(2008). Available from: https://bitcoin.org/bitcoin.pdf.
    [2] M. K. M. Ly, Coining bitcoin's Legal-Bits: Examining the regulatory framework for bitcoin and virtual Currencies, Harv. J. L. Tech., 27 (2014), 587-608.
    [3] A. Zohar, bitcoin: Under the Hood, Communicat. ACM, 58 (2015), 104-113.
    [4] V. Savvas, P. Perikles, R. Maria, bitcoin value analysis based on cross-correlations, J. Int. Bank. Commerce, 22 (2017), 1-12.
    [5] S. Higgins. $300 Billion: Bitcoin price boosts crypto market value to record high, 2018. Available from: https://www.coindesk.com/.
    [6] M. A. Khan and K. Salah, IoT security: Review, blockchain solutions, and open challenges, Future Generat. Comput. Syst., 82 (2018), 395-411.
    [7] A. Suliman, Z. Husain, M. Abououf, et al., Monetization of IoT Data using Smart Contracts, IET Networks, 8 (2019), 32-37.
    [8] M. Conti, C. Lal and S. Ruj, A Survey on Security and Privacy Issues of bitcoin., 2017. Available from: https://arxiv.org/abs/1706.00916.
    [9] Y. X. WANG and J. T. Gao, A Regulation Scheme Based on the CiphertextPolicy Hierarchical Attribute-Based Encryption in Bitcoin System, IEEE Access, 6 (2018), 16267-16278.
    [10] A. Miller, A. Kosba, J. Katz, et al., Nonoutsourceable Scratch-Off Puzzles to Discourage bitcoin Mining Coalitions, Proc. 22nd ACM SIGSAC Conf. Comput. Comm. Secur. (CCS), Denver, Colorado, USA, October, 2015 (2015), 680-691.
    [11] G. O. Karame, E. Androulaki, M. Roeschlin, et al., Misbehavior in bitcoin: A study of doublespending and accountability, ACM T. Ioform. Syst. SE., 18 (2015), 1-32.
    [12] G. O. Karame, E. Androulaki, M. Roeschlin, et al., Misbehavior in Bitcoin: A Study of DoubleSpending and Accountability ACM Transact. Inform. Syst. Secur., 18 (2015), 1-32.
    [13] Khaled Salah, M. H. U. Rehman, N. Nizamuddin, Blockchain for AI: Review and open research challenges, IEEE Access, 7 (2019), 10127-10148.
    [14] I. Eyal and E. G. Sirer, Majority is not enough: Bitcoin mining is vulnerable, Proc. Int. Conf. Fina. Cryptol. Secur., Christ Church, Barbados, March, 2014 (2014), 436- 454.
    [15] H. R. HASAN and K. Salah, Combating deepfake videos using blockchain and smart contracts, IEEE Access, 7 (2019), 41596-41606.
    [16] B. Johnson, A. Laszka, J. Grossklags, et al., Game-Theoretic analysis of DDoS attacks against bitcoin mining pools, Proc. Int. Conf. Fina. Cryptol. Secur., Christ Church, Barbados, March, 2014 (2014), 72-86.
    [17] E. Heilman, A. Kendler, A. Zohar, et al., Eclipse attacks on bitcoin's peer-to-peer network, Porc. 24th USENIX Symp. Secur., Washington, D.C., USA, 2015 (2015), 129-144.
    [18] K. Nayak, S. Kumar, A. Miller, et al., Stubborn mining: Generalizing selfish mining and combining with an eclipse attack, Proc. IEEE Euro. Symp. Secur Privacy (Euro S&P), Saarbrucken, Germany, March, 2016 (2016), 1-16.
    [19] N. T. Courtois and L. Bahack, On subversive miner strategies and block withholding attack in bitcoin digital currency, 2014. Available from: https://arxiv.org/abs/1402.1718.
    [20] M. Rosenfeld, Analysis of bitcoin pooled mining reward systems. Accessed: Nov.30, 2018, 2011. Available from: https://arxiv.org/abs/1112.4980.
    [21] D. Wu, X. D. Liu, X. B. Yan, et al., Equilibrium analysis of bitcoin block withholding attack: A generalized model, Reliabil. Eng. Syst. Safety, 5 (2019), 1-32.
    [22] L. Luu, R. Saha, I. Parameshwaran, et al., On power splitting games in distributed computation: The case of bitcoin pooled mining, Proc. IEEE Symp. 28th Comput. Secur. Found (CSF), Verona, Italy, July, 2015 (2015), 1-18.
    [23] I. Eyal, The miner's dilemma, Proc. IEEE Symp. Secur. Privacy (SP), San Jose, CA, USA, July, 2015 (2015), 89-103.
    [24] S. Bag, S. Ruj and K. Sakurai, Bitcoin block withholding attack: Analysis and mitigation, IEEE Trans. Inf. Forensic Secur., 12 (2017), 1967-1978.
    [25] N. T. Courtois and L. Bahack, On subversive miner strategies and block withholding attack in bitcoin digital currency, 2014. Available from: Available:https://arxiv.org/abs/1402.1718.
    [26] A. Laszka, B. Johnson and J. Grossklags, When bitcoin mining pools run dry, Proc. Int. Conf. Fina. Cryptol Secur., San Juan, Puerto Rico, January, 2015 (2015), 63-77.
    [27] S. Shen, H. Li, R. Han, et al., Differential game-based strategies for preventing malware propagation in wireless sensor networks, IEEE Trans. Inf. Forensic Secur., 9 (2014), 1962-1973.
    [28] J. Tuwiner, Bitcoin Mining Pools, 2018. Available from: https://www.buybitcoinworldwide.com.
    [29] O. Schrijvers, J. Bonneau, D. Boneh, et al., Incentive compatibility of bitcoin mining pool reward functions, Proc. Int. Conf. Fina. Cryptol.Secur., Christ Church, Barbados, 2016 (2016), 477- 498.
    [30] S. Bag and K. Sakurai, Yet another note on block withholding attack on bitcoin mining pools, Proc. Int. Conf. Inf. Secur.(ISC), Honolulu, HI, USA, 2016 (2016), 167-180.
    [31] J. S. R. Jangn and C. T. Sun, Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence, Prentice-Hall Inc., Upper Saddle River, NJ, 1996 (1996).
    [32] M. Mizumoto and K. Tanaka, Some properties of fuzzy sets of type-2, Inform. Control, 31 (1976), 312-340.
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5913) PDF downloads(558) Cited by(4)

Article outline

Figures and Tables

Figures(7)  /  Tables(3)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog