Dynamic optimization algorithms for enhancing blockchain network resilience against distributed attacks
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Abstract
This research introduces a dynamic optimization algorithm designed to enhance blockchain network resilience against distributed attacks such as Distributed Denial of Service (DDoS), Sybil, and eclipse attacks. The primary objective is to develop a real-time, adaptive control strategy that minimizes network performance degradation while dynamically responding to evolving threats. The research design integrates multi-objective optimization, game theory, and reinforcement learning to formulate a defense strategy that adapts to adversarial conditions. The methodology is based on a modified state-space model, where the blockchain's performance is represented by a system of dynamic equations influenced by both control actions (defensive measures) and attack vectors. The optimization problem is formulated to minimize a cost function that balances network resilience and resource usage. A numerical example is presented to validate the model, demonstrating the algorithm’s effectiveness in maintaining network performance under attack by adjusting defense mechanisms in real-time. The main results indicate that the proposed method significantly reduces the impact of distributed attacks while ensuring efficient resource allocation. In conclusion, this research offers a novel framework for enhancing blockchain security, with implications for real-world applications in decentralized systems, financial services, and critical infrastructure. Future work will address the scalability of the algorithm and explore more advanced reinforcement learning techniques to handle more complex and unpredictable attack patterns.
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How to Cite
Riandari, F., Afrisawati, A., Afifa, R. M., Syahputra, R., & Ginting, R. (2024). Dynamic optimization algorithms for enhancing blockchain network resilience against distributed attacks. International Journal of Basic and Applied Science, 13(2), 96–111. https://doi.org/10.35335/ijobas.v13i2.499
References
M. Krichen, M. Ammi, A. Mihoub, and M. Almutiq, “Blockchain for modern applications: A survey,” Sensors, vol. 22, no. 14, p. 5274, 2022, doi: https://doi.org/10.3390/s22145274.
M. Pilkington, “Blockchain technology: principles and applications,” in Research handbook on digital transformations, Edward Elgar Publishing, 2016, pp. 225–253. doi: https://doi.org/10.4337/9781784717766.00019.
S. El Haddouti and M. D. E.-C. El Kettani, “Analysis of identity management systems using blockchain technology,” in 2019 International Conference on Advanced Communication Technologies and Networking (CommNet), IEEE, 2019, pp. 1–7. doi: https://doi.org/10.1109/COMMNET.2019.8742375.
D. B. Rawat, V. Chaudhary, and R. Doku, “Blockchain technology: Emerging applications and use cases for secure and trustworthy smart systems,” J. Cybersecurity Priv., vol. 1, no. 1, pp. 4–18, 2020, doi: https://doi.org/10.3390/jcp1010002.
J. Ali and S. Sofi, “Ensuring security and transparency in distributed communication in iot ecosystems using blockchain technology: Protocols, applications and challenges,” Int. J. Comput. Digit. Syst., 2021, doi: https://dx.doi.org/10.12785/ijcds/110101.
S. Singh, A. S. M. S. Hosen, and B. Yoon, “Blockchain security attacks, challenges, and solutions for the future distributed iot network,” Ieee Access, vol. 9, no. 14, pp. 13938–13959, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3051602.
M. Saad et al., “Exploring the attack surface of blockchain: A comprehensive survey,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1977–2008, 2020, doi: https://doi.org/10.1109/COMST.2020.2975999.
Z. Shah, I. Ullah, H. Li, A. Levula, and K. Khurshid, “Blockchain based solutions to mitigate distributed denial of service (DDoS) attacks in the Internet of Things (IoT): A survey,” Sensors, vol. 22, no. 3, p. 1094, 2022, doi: https://doi.org/10.3390/s22031094.
R. Chaganti et al., “A comprehensive review of denial of service attacks in blockchain ecosystem and open challenges,” IEEE Access, vol. 10, no. 9, pp. 96538–96555, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3205019.
P. I. R. Grammatikis, P. G. Sarigiannidis, and I. D. Moscholios, “Securing the Internet of Things: Challenges, threats and solutions,” Internet of Things, vol. 5, no. 3, pp. 41–70, 2019, doi: https://doi.org/10.1016/j.iot.2018.11.003.
Y. Ashibani and Q. H. Mahmoud, “Cyber physical systems security: Analysis, challenges and solutions,” Comput. Secur., vol. 68, no. 7, pp. 81–97, 2017, doi: https://doi.org/10.1016/j.cose.2017.04.005.
V. Wylde et al., “Cybersecurity, data privacy and blockchain: A review,” SN Comput. Sci., vol. 3, no. 2, p. 127, 2022, doi: https://doi.org/10.1007/s42979-022-01020-4.
E. A. Parn and D. Edwards, “Cyber threats confronting the digital built environment: Common data environment vulnerabilities and block chain deterrence,” Eng. Constr. Archit. Manag., vol. 26, no. 2, pp. 245–266, 2019, doi: https://doi.org/10.1108/ECAM-03-2018-0101.
W. Gao, W. G. Hatcher, and W. Yu, “A survey of blockchain: Techniques, applications, and challenges,” in 2018 27th international conference on computer communication and networks (ICCCN), IEEE, 2018, pp. 1–11. doi: https://doi.org/10.1109/ICCCN.2018.8487348.
P. Zhang and M. Zhou, “Security and trust in blockchains: Architecture, key technologies, and open issues,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 3, pp. 790–801, 2020, doi: https://doi.org/10.1109/TCSS.2020.2990103.
S. Ahmadjee, C. Mera-Gómez, R. Bahsoon, and R. Kazman, “A study on blockchain architecture design decisions and their security attacks and threats,” ACM Trans. Softw. Eng. Methodol., vol. 31, no. 2, pp. 1–45, 2022, doi: https://doi.org/10.1145/3502740.
J. Kaur Chahal, A. Bhandari, and S. Behal, “Distributed denial of service attacks: a threat or challenge,” New Rev. Inf. Netw., vol. 24, no. 1, pp. 31–103, 2019, doi: https://doi.org/10.1080/13614576.2019.1611468.
Q. Yan, F. R. Yu, Q. Gong, and J. Li, “Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges,” IEEE Commun. Surv. tutorials, vol. 18, no. 1, pp. 602–622, 2015, doi: https://doi.org/10.1109/COMST.2015.2487361.
D. Geepthi, C. C. Columbus, and C. Jeyanthi, “Peer to peer sybil and eclipse attack detection via fuzzy kademlia,” J. Intell. Fuzzy Syst., vol. 44, no. 4, pp. 6925–6937, 2023, doi: 10.3233/JIFS-222802.
K. Hameed, M. Barika, S. Garg, M. B. Amin, and B. Kang, “A taxonomy study on securing Blockchain-based Industrial applications: An overview, application perspectives, requirements, attacks, countermeasures, and open issues,” J. Ind. Inf. Integr., vol. 26, no. 3, p. 100312, 2022, doi: https://doi.org/10.1016/j.jii.2021.100312.
I. Homoliak, S. Venugopalan, D. Reijsbergen, Q. Hum, R. Schumi, and P. Szalachowski, “The security reference architecture for blockchains: Toward a standardized model for studying vulnerabilities, threats, and defenses,” IEEE Commun. Surv. Tutorials, vol. 23, no. 1, pp. 341–390, 2020, doi: https://doi.org/10.1109/COMST.2020.3033665.
M. Calvo and M. Beltrán, “A model for risk-based adaptive security controls,” Comput. Secur., vol. 115, no. 3, p. 102612, 2022, doi: https://doi.org/10.1016/j.cose.2022.102612.
P. Nespoli, D. Díaz-López, and F. G. Mármol, “Cyberprotection in IoT environments: A dynamic rule-based solution to defend smart devices,” J. Inf. Secur. Appl., vol. 60, no. 8, p. 102878, 2021, doi: https://doi.org/10.1016/j.jisa.2021.102878.
A. Kiran, P. Mathivanan, M. Mahdal, K. Sairam, D. Chauhan, and V. Talasila, “Enhancing data security in IoT networks with blockchain-based management and adaptive clustering techniques,” Mathematics, vol. 11, no. 9, p. 2073, 2023, doi: https://doi.org/10.3390/math11092073.
M. Mylrea and S. N. G. Gourisetti, “Blockchain for smart grid resilience: Exchanging distributed energy at speed, scale and security,” in 2017 Resilience Week (RWS), IEEE, 2017, pp. 18–23. doi: https://doi.org/10.1109/RWEEK.2017.8088642.
B. Saha, M. M. Hasan, N. Anjum, S. Tahora, A. Siddika, and H. Shahriar, “Protecting the decentralized future: An exploration of common blockchain attacks and their countermeasures,” 2023. doi: https://doi.org/10.48550/arXiv.2306.11884.
K. Dwivedi, A. Agrawal, A. Bhatia, and K. Tiwari, “A Novel Classification of Attacks on Blockchain Layers: Vulnerabilities, Attacks, Mitigations, and Research Directions,” 2024. doi: https://doi.org/10.48550/arXiv.2404.18090.
T. Ault, S. Krahn, and A. Croff, “Thorium fuel cycle research and literature: Trends and insights from eight decades of diverse projects and evolving priorities,” Ann. Nucl. Energy, vol. 110, no. 12, pp. 726–738, 2017, doi: https://doi.org/10.1016/j.anucene.2017.06.026.
T. Murakami, “A historical review and analysis on the selection of nuclear reactor types and implications to development programs for advanced reactors; A Japanese study,” Energy Reports, vol. 7, no. 11, pp. 3428–3436, 2021, doi: https://doi.org/10.1016/j.egyr.2021.05.049.
M. Anisetti, C. Ardagna, M. Cremonini, E. Damiani, J. Sessa, and L. Costa, “Security threat landscape,” White Pap. Secur. Threat., 2020.
T. UcedaVelez and M. M. Morana, Risk Centric Threat Modeling: process for attack simulation and threat analysis. John Wiley & Sons, 2015.
M. P. S. Bhatia and S. R. Sangwan, “Soft computing for anomaly detection and prediction to mitigate IoT-based real-time abuse,” Pers. Ubiquitous Comput., vol. 28, no. 1, pp. 123–133, 2024, doi: https://doi.org/10.1007/s00779-021-01567-8.
O. A. Ajala, C. C. Okoye, O. C. Ofodile, C. A. Arinze, and O. D. Daraojimba, “Review of AI and machine learning applications to predict and Thwart cyber-attacks in real-time,” Magna Sci. Adv. Res. Rev., vol. 10, no. 1, pp. 312–320, 2024, doi: https://doi.org/10.30574/msarr.2024.10.1.0037.
B. R. Maddireddy and B. R. Maddireddy, “Evolutionary Algorithms in AI-Driven Cybersecurity Solutions for Adaptive Threat Mitigation,” Int. J. Adv. Eng. Technol. Innov., vol. 1, no. 2, pp. 17–43, 2021, doi: https://ijaeti.com/index.php/Journal/article/view/319.
K. Venkatesan and S. B. Rahayu, “Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques,” Sci. Rep., vol. 14, no. 1, p. 1149, 2024, doi: https://doi.org/10.1038/s41598-024-51578-7.
K. Zkik, A. Belhadi, S. Kamble, M. Venkatesh, M. Oudani, and A. Sebbar, “Cyber resilience framework for online retail using explainable deep learning approaches and blockchain-based consensus protocol,” Decis. Support Syst., vol. 182, no. 7, p. 114253, 2024, doi: https://doi.org/10.1016/j.dss.2024.114253.
J. Cheng, L. Xie, X. Tang, N. Xiong, and B. Liu, “A survey of security threats and defense on Blockchain,” Multimed. Tools Appl., vol. 80, no. 4, pp. 30623–30652, 2021, doi: https://doi.org/10.1007/s11042-020-09368-6.
X. Li et al., “Blockchain security threats and collaborative defense: A literature review,” p. 365, 2023. doi: https://doi.org/10.32604/cmc.2023.040596.
M. Javed, N. Tariq, M. Ashraf, F. A. Khan, M. Asim, and M. Imran, “Securing smart healthcare cyber-physical systems against blackhole and greyhole attacks using a blockchain-enabled gini index framework,” Sensors, vol. 23, no. 23, p. 9372, 2023, doi: https://doi.org/10.3390/s23239372.
A. Nazir et al., “Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration,” J. King Saud Univ. Inf. Sci., vol. 36, no. 2, p. 101939, 2024, doi: https://doi.org/10.1016/j.jksuci.2024.101939.
S. Cherbal, A. Zier, S. Hebal, L. Louail, and B. Annane, “Security in internet of things: a review on approaches based on blockchain, machine learning, cryptography, and quantum computing,” J. Supercomput., vol. 80, no. 3, pp. 3738–3816, 2024, doi: https://doi.org/10.1007/s11227-023-05616-2.
Z. Zulkifl et al., “FBASHI: Fuzzy and blockchain-based adaptive security for healthcare IoTs,” IEEE Access, vol. 10, no. 2, pp. 15644–15656, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3149046.
S. El Kafhali, I. El Mir, and M. Hanini, “Security threats, defense mechanisms, challenges, and future directions in cloud computing,” Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 223–246, 2022, doi: https://doi.org/10.1007/s11831-021-09573-y.
Y. Deng, T. Zhang, G. Lou, X. Zheng, J. Jin, and Q.-L. Han, “Deep learning-based autonomous driving systems: A survey of attacks and defenses,” IEEE Trans. Ind. Informatics, vol. 17, no. 12, pp. 7897–7912, 2021, doi: https://doi.org/10.1109/TII.2021.3071405.
T. Hester and P. Stone, “Texplore: real-time sample-efficient reinforcement learning for robots,” Mach. Learn., vol. 90, no. 9, pp. 385–429, 2013, doi: https://doi.org/10.1007/s10994-012-5322-7.
N. Jiang, Y. Deng, A. Nallanathan, and J. A. Chambers, “Reinforcement learning for real-time optimization in NB-IoT networks,” IEEE J. Sel. Areas Commun., vol. 37, no. 6, pp. 1424–1440, 2019, doi: https://doi.org/10.1109/JSAC.2019.2904366.
Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu, and V. C. M. Leung, “A survey on security threats and defensive techniques of machine learning: A data driven view,” IEEE access, vol. 6, no. 2, pp. 12103–12117, 2018, doi: https://doi.org/10.1109/ACCESS.2018.2805680.
M. A. Shyaa, N. F. Ibrahim, Z. Zainol, R. Abdullah, M. Anbar, and L. Alzubaidi, “Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection systems,” Eng. Appl. Artif. Intell., vol. 137, no. 1, p. 109143, 2024, doi: https://doi.org/10.1016/j.engappai.2024.109143.
C. Douligeris and A. Mitrokotsa, “DDoS attacks and defense mechanisms: classification and state-of-the-art,” Comput. networks, vol. 44, no. 5, pp. 643–666, 2004, doi: https://doi.org/10.1016/j.comnet.2003.10.003.
L. Saldana, “The stages of implementation completion for evidence-based practice: protocol for a mixed methods study,” Implement. Sci., vol. 9, no. 3, pp. 1–11, 2014, doi: https://doi.org/10.1186/1748-5908-9-43.
R. Jabbar et al., “Blockchain technology for intelligent transportation systems: A systematic literature review,” IEEE Access, vol. 10, no. 5, pp. 20995–21031, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3149958.
A. Tandon, A. Dhir, A. K. M. N. Islam, and M. Mäntymäki, “Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda,” Comput. Ind., vol. 122, no. 11, p. 103290, 2020, doi: https://doi.org/10.1016/j.compind.2020.103290.
B. Cao et al., “Blockchain systems, technologies, and applications: A methodology perspective,” IEEE Commun. Surv. Tutorials, vol. 25, no. 1, pp. 353–385, 2022, doi: https://doi.org/10.1109/COMST.2022.3204702.
K. Saadat, “Flexible Blockchain Framework for Dynamic Cluster-based Applications,” University of Surrey, 2024.
P. Zappalà, M. Belotti, M. Potop-Butucaru, and S. Secci, “Game theoretical framework for analyzing blockchains robustness,” in 35th International Symposium on Distributed Computing (DISC 2021), Sorbonne Université, CNRS, Laboratoire d’Informatique de Paris 6, LIP6 …, 2021, pp. 41–42. doi: 10.4230/LIPIcs.DISC.2021.42.
Q. Wang, W. Li, and A. Mohajer, “Load-aware continuous-time optimization for multi-agent systems: Toward dynamic resource allocation and real-time adaptability,” Comput. Networks, vol. 250, no. 8, p. 110526, 2024, doi: https://doi.org/10.1016/j.comnet.2024.110526.
A. G. Barto, S. J. Bradtke, and S. P. Singh, “Learning to act using real-time dynamic programming,” Artif. Intell., vol. 72, no. 1–2, pp. 81–138, 1995, doi: https://doi.org/10.1016/0004-3702(94)00011-O.
V. Bala, E. Duesterwald, and S. Banerjia, “Dynamo: A transparent dynamic optimization system,” in Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation, 2000, pp. 1–12. doi: https://doi.org/10.1145/349299.349303.
Z. Liu et al., “A survey on blockchain: A game theoretical perspective,” IEEE Access, vol. 7, no. 7, pp. 47615–47643, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2909924.
A. Attiah, M. Chatterjee, and C. C. Zou, “A game theoretic approach to model cyber attack and defense strategies,” in 2018 IEEE International Conference on Communications (ICC), IEEE, 2018, pp. 1–7. doi: https://doi.org/10.1109/ICC.2018.8422719.
G. Fan, H. Yu, L. Chen, and D. Liu, “A game theoretic method to model and evaluate attack-defense strategy in cloud computing,” in 2013 IEEE International Conference on Services Computing, IEEE, 2013, pp. 659–666. doi: https://doi.org/10.1109/SCC.2013.110.
Q. Wang, W. Tai, Y. Tang, M. Ni, and S. You, “A two-layer game theoretical attack-defense model for a false data injection attack against power systems,” Int. J. Electr. Power Energy Syst., vol. 104, no. 4, pp. 169–177, 2019, doi: https://doi.org/10.1016/j.ijepes.2018.07.007.
M. Mavrovouniotis, C. Li, and S. Yang, “A survey of swarm intelligence for dynamic optimization: Algorithms and applications,” Swarm Evol. Comput., vol. 33, no. 4, pp. 1–17, 2017, doi: https://doi.org/10.1016/j.swevo.2016.12.005.
T. T. Nguyen, S. Yang, and J. Branke, “Evolutionary dynamic optimization: A survey of the state of the art,” Swarm Evol. Comput., vol. 6, pp. 1–24, 2012, doi: https://doi.org/10.1016/j.swevo.2012.05.001.
V. Deshpande, H. Badis, and L. George, “Efficient topology control of blockchain peer to peer network based on SDN paradigm,” Peer-to-Peer Netw. Appl., vol. 15, no. 1, pp. 267–289, 2022, doi: https://doi.org/10.1007/s12083-021-01248-6.
G. Jayabalasamy, C. Pujol, and K. Latha Bhaskaran, “Application of Graph Theory for Blockchain Technologies,” Mathematics, vol. 12, no. 8, p. 1133, 2024, doi: https://doi.org/10.3390/math12081133.
M. Swan, “Blockchain economic networks: Economic network theory—Systemic risk and blockchain technology,” in Business Transformation through Blockchain: Volume I, Springer, 2019, pp. 3–45. doi: https://doi.org/10.1007/978-3-319-98911-2_1.
M. Pirani, A. Mitra, and S. Sundaram, “Graph-theoretic approaches for analyzing the resilience of distributed control systems: A tutorial and survey,” Automatica, vol. 157, no. 3, p. 111264, 2023, doi: https://doi.org/10.1016/j.automatica.2023.111264.
V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, and J. Pineau, “An introduction to deep reinforcement learning,” Found. Trends® Mach. Learn., vol. 11, no. 3–4, pp. 219–354, 2018, doi: http://dx.doi.org/10.1561/2200000071.
H. Dong, H. Dong, Z. Ding, S. Zhang, and T. Chang, Deep Reinforcement Learning. Springer, 2020. doi: https://doi.org/10.1007/978-981-15-4095-0.
V. Singh, S.-S. Chen, M. Singhania, B. Nanavati, and A. Gupta, “How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda,” Int. J. Inf. Manag. Data Insights, vol. 2, no. 2, p. 100094, 2022, doi: https://doi.org/10.1016/j.jjimei.2022.100094.
A. Kosba, A. Miller, E. Shi, Z. Wen, and C. Papamanthou, “Hawk: The blockchain model of cryptography and privacy-preserving smart contracts,” in 2016 IEEE symposium on security and privacy (SP), IEEE, 2016, pp. 839–858. doi: https://doi.org/10.1109/SP.2016.55.
G. Zheng et al., “DRN: A deep reinforcement learning framework for news recommendation,” in Proceedings of the 2018 world wide web conference, 2018, pp. 167–176. doi: https://doi.org/10.1145/3178876.3185994.
M. Pilkington, “Blockchain technology: principles and applications,” in Research handbook on digital transformations, Edward Elgar Publishing, 2016, pp. 225–253. doi: https://doi.org/10.4337/9781784717766.00019.
S. El Haddouti and M. D. E.-C. El Kettani, “Analysis of identity management systems using blockchain technology,” in 2019 International Conference on Advanced Communication Technologies and Networking (CommNet), IEEE, 2019, pp. 1–7. doi: https://doi.org/10.1109/COMMNET.2019.8742375.
D. B. Rawat, V. Chaudhary, and R. Doku, “Blockchain technology: Emerging applications and use cases for secure and trustworthy smart systems,” J. Cybersecurity Priv., vol. 1, no. 1, pp. 4–18, 2020, doi: https://doi.org/10.3390/jcp1010002.
J. Ali and S. Sofi, “Ensuring security and transparency in distributed communication in iot ecosystems using blockchain technology: Protocols, applications and challenges,” Int. J. Comput. Digit. Syst., 2021, doi: https://dx.doi.org/10.12785/ijcds/110101.
S. Singh, A. S. M. S. Hosen, and B. Yoon, “Blockchain security attacks, challenges, and solutions for the future distributed iot network,” Ieee Access, vol. 9, no. 14, pp. 13938–13959, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3051602.
M. Saad et al., “Exploring the attack surface of blockchain: A comprehensive survey,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1977–2008, 2020, doi: https://doi.org/10.1109/COMST.2020.2975999.
Z. Shah, I. Ullah, H. Li, A. Levula, and K. Khurshid, “Blockchain based solutions to mitigate distributed denial of service (DDoS) attacks in the Internet of Things (IoT): A survey,” Sensors, vol. 22, no. 3, p. 1094, 2022, doi: https://doi.org/10.3390/s22031094.
R. Chaganti et al., “A comprehensive review of denial of service attacks in blockchain ecosystem and open challenges,” IEEE Access, vol. 10, no. 9, pp. 96538–96555, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3205019.
P. I. R. Grammatikis, P. G. Sarigiannidis, and I. D. Moscholios, “Securing the Internet of Things: Challenges, threats and solutions,” Internet of Things, vol. 5, no. 3, pp. 41–70, 2019, doi: https://doi.org/10.1016/j.iot.2018.11.003.
Y. Ashibani and Q. H. Mahmoud, “Cyber physical systems security: Analysis, challenges and solutions,” Comput. Secur., vol. 68, no. 7, pp. 81–97, 2017, doi: https://doi.org/10.1016/j.cose.2017.04.005.
V. Wylde et al., “Cybersecurity, data privacy and blockchain: A review,” SN Comput. Sci., vol. 3, no. 2, p. 127, 2022, doi: https://doi.org/10.1007/s42979-022-01020-4.
E. A. Parn and D. Edwards, “Cyber threats confronting the digital built environment: Common data environment vulnerabilities and block chain deterrence,” Eng. Constr. Archit. Manag., vol. 26, no. 2, pp. 245–266, 2019, doi: https://doi.org/10.1108/ECAM-03-2018-0101.
W. Gao, W. G. Hatcher, and W. Yu, “A survey of blockchain: Techniques, applications, and challenges,” in 2018 27th international conference on computer communication and networks (ICCCN), IEEE, 2018, pp. 1–11. doi: https://doi.org/10.1109/ICCCN.2018.8487348.
P. Zhang and M. Zhou, “Security and trust in blockchains: Architecture, key technologies, and open issues,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 3, pp. 790–801, 2020, doi: https://doi.org/10.1109/TCSS.2020.2990103.
S. Ahmadjee, C. Mera-Gómez, R. Bahsoon, and R. Kazman, “A study on blockchain architecture design decisions and their security attacks and threats,” ACM Trans. Softw. Eng. Methodol., vol. 31, no. 2, pp. 1–45, 2022, doi: https://doi.org/10.1145/3502740.
J. Kaur Chahal, A. Bhandari, and S. Behal, “Distributed denial of service attacks: a threat or challenge,” New Rev. Inf. Netw., vol. 24, no. 1, pp. 31–103, 2019, doi: https://doi.org/10.1080/13614576.2019.1611468.
Q. Yan, F. R. Yu, Q. Gong, and J. Li, “Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges,” IEEE Commun. Surv. tutorials, vol. 18, no. 1, pp. 602–622, 2015, doi: https://doi.org/10.1109/COMST.2015.2487361.
D. Geepthi, C. C. Columbus, and C. Jeyanthi, “Peer to peer sybil and eclipse attack detection via fuzzy kademlia,” J. Intell. Fuzzy Syst., vol. 44, no. 4, pp. 6925–6937, 2023, doi: 10.3233/JIFS-222802.
K. Hameed, M. Barika, S. Garg, M. B. Amin, and B. Kang, “A taxonomy study on securing Blockchain-based Industrial applications: An overview, application perspectives, requirements, attacks, countermeasures, and open issues,” J. Ind. Inf. Integr., vol. 26, no. 3, p. 100312, 2022, doi: https://doi.org/10.1016/j.jii.2021.100312.
I. Homoliak, S. Venugopalan, D. Reijsbergen, Q. Hum, R. Schumi, and P. Szalachowski, “The security reference architecture for blockchains: Toward a standardized model for studying vulnerabilities, threats, and defenses,” IEEE Commun. Surv. Tutorials, vol. 23, no. 1, pp. 341–390, 2020, doi: https://doi.org/10.1109/COMST.2020.3033665.
M. Calvo and M. Beltrán, “A model for risk-based adaptive security controls,” Comput. Secur., vol. 115, no. 3, p. 102612, 2022, doi: https://doi.org/10.1016/j.cose.2022.102612.
P. Nespoli, D. Díaz-López, and F. G. Mármol, “Cyberprotection in IoT environments: A dynamic rule-based solution to defend smart devices,” J. Inf. Secur. Appl., vol. 60, no. 8, p. 102878, 2021, doi: https://doi.org/10.1016/j.jisa.2021.102878.
A. Kiran, P. Mathivanan, M. Mahdal, K. Sairam, D. Chauhan, and V. Talasila, “Enhancing data security in IoT networks with blockchain-based management and adaptive clustering techniques,” Mathematics, vol. 11, no. 9, p. 2073, 2023, doi: https://doi.org/10.3390/math11092073.
M. Mylrea and S. N. G. Gourisetti, “Blockchain for smart grid resilience: Exchanging distributed energy at speed, scale and security,” in 2017 Resilience Week (RWS), IEEE, 2017, pp. 18–23. doi: https://doi.org/10.1109/RWEEK.2017.8088642.
B. Saha, M. M. Hasan, N. Anjum, S. Tahora, A. Siddika, and H. Shahriar, “Protecting the decentralized future: An exploration of common blockchain attacks and their countermeasures,” 2023. doi: https://doi.org/10.48550/arXiv.2306.11884.
K. Dwivedi, A. Agrawal, A. Bhatia, and K. Tiwari, “A Novel Classification of Attacks on Blockchain Layers: Vulnerabilities, Attacks, Mitigations, and Research Directions,” 2024. doi: https://doi.org/10.48550/arXiv.2404.18090.
T. Ault, S. Krahn, and A. Croff, “Thorium fuel cycle research and literature: Trends and insights from eight decades of diverse projects and evolving priorities,” Ann. Nucl. Energy, vol. 110, no. 12, pp. 726–738, 2017, doi: https://doi.org/10.1016/j.anucene.2017.06.026.
T. Murakami, “A historical review and analysis on the selection of nuclear reactor types and implications to development programs for advanced reactors; A Japanese study,” Energy Reports, vol. 7, no. 11, pp. 3428–3436, 2021, doi: https://doi.org/10.1016/j.egyr.2021.05.049.
M. Anisetti, C. Ardagna, M. Cremonini, E. Damiani, J. Sessa, and L. Costa, “Security threat landscape,” White Pap. Secur. Threat., 2020.
T. UcedaVelez and M. M. Morana, Risk Centric Threat Modeling: process for attack simulation and threat analysis. John Wiley & Sons, 2015.
M. P. S. Bhatia and S. R. Sangwan, “Soft computing for anomaly detection and prediction to mitigate IoT-based real-time abuse,” Pers. Ubiquitous Comput., vol. 28, no. 1, pp. 123–133, 2024, doi: https://doi.org/10.1007/s00779-021-01567-8.
O. A. Ajala, C. C. Okoye, O. C. Ofodile, C. A. Arinze, and O. D. Daraojimba, “Review of AI and machine learning applications to predict and Thwart cyber-attacks in real-time,” Magna Sci. Adv. Res. Rev., vol. 10, no. 1, pp. 312–320, 2024, doi: https://doi.org/10.30574/msarr.2024.10.1.0037.
B. R. Maddireddy and B. R. Maddireddy, “Evolutionary Algorithms in AI-Driven Cybersecurity Solutions for Adaptive Threat Mitigation,” Int. J. Adv. Eng. Technol. Innov., vol. 1, no. 2, pp. 17–43, 2021, doi: https://ijaeti.com/index.php/Journal/article/view/319.
K. Venkatesan and S. B. Rahayu, “Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques,” Sci. Rep., vol. 14, no. 1, p. 1149, 2024, doi: https://doi.org/10.1038/s41598-024-51578-7.
K. Zkik, A. Belhadi, S. Kamble, M. Venkatesh, M. Oudani, and A. Sebbar, “Cyber resilience framework for online retail using explainable deep learning approaches and blockchain-based consensus protocol,” Decis. Support Syst., vol. 182, no. 7, p. 114253, 2024, doi: https://doi.org/10.1016/j.dss.2024.114253.
J. Cheng, L. Xie, X. Tang, N. Xiong, and B. Liu, “A survey of security threats and defense on Blockchain,” Multimed. Tools Appl., vol. 80, no. 4, pp. 30623–30652, 2021, doi: https://doi.org/10.1007/s11042-020-09368-6.
X. Li et al., “Blockchain security threats and collaborative defense: A literature review,” p. 365, 2023. doi: https://doi.org/10.32604/cmc.2023.040596.
M. Javed, N. Tariq, M. Ashraf, F. A. Khan, M. Asim, and M. Imran, “Securing smart healthcare cyber-physical systems against blackhole and greyhole attacks using a blockchain-enabled gini index framework,” Sensors, vol. 23, no. 23, p. 9372, 2023, doi: https://doi.org/10.3390/s23239372.
A. Nazir et al., “Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration,” J. King Saud Univ. Inf. Sci., vol. 36, no. 2, p. 101939, 2024, doi: https://doi.org/10.1016/j.jksuci.2024.101939.
S. Cherbal, A. Zier, S. Hebal, L. Louail, and B. Annane, “Security in internet of things: a review on approaches based on blockchain, machine learning, cryptography, and quantum computing,” J. Supercomput., vol. 80, no. 3, pp. 3738–3816, 2024, doi: https://doi.org/10.1007/s11227-023-05616-2.
Z. Zulkifl et al., “FBASHI: Fuzzy and blockchain-based adaptive security for healthcare IoTs,” IEEE Access, vol. 10, no. 2, pp. 15644–15656, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3149046.
S. El Kafhali, I. El Mir, and M. Hanini, “Security threats, defense mechanisms, challenges, and future directions in cloud computing,” Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 223–246, 2022, doi: https://doi.org/10.1007/s11831-021-09573-y.
Y. Deng, T. Zhang, G. Lou, X. Zheng, J. Jin, and Q.-L. Han, “Deep learning-based autonomous driving systems: A survey of attacks and defenses,” IEEE Trans. Ind. Informatics, vol. 17, no. 12, pp. 7897–7912, 2021, doi: https://doi.org/10.1109/TII.2021.3071405.
T. Hester and P. Stone, “Texplore: real-time sample-efficient reinforcement learning for robots,” Mach. Learn., vol. 90, no. 9, pp. 385–429, 2013, doi: https://doi.org/10.1007/s10994-012-5322-7.
N. Jiang, Y. Deng, A. Nallanathan, and J. A. Chambers, “Reinforcement learning for real-time optimization in NB-IoT networks,” IEEE J. Sel. Areas Commun., vol. 37, no. 6, pp. 1424–1440, 2019, doi: https://doi.org/10.1109/JSAC.2019.2904366.
Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu, and V. C. M. Leung, “A survey on security threats and defensive techniques of machine learning: A data driven view,” IEEE access, vol. 6, no. 2, pp. 12103–12117, 2018, doi: https://doi.org/10.1109/ACCESS.2018.2805680.
M. A. Shyaa, N. F. Ibrahim, Z. Zainol, R. Abdullah, M. Anbar, and L. Alzubaidi, “Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection systems,” Eng. Appl. Artif. Intell., vol. 137, no. 1, p. 109143, 2024, doi: https://doi.org/10.1016/j.engappai.2024.109143.
C. Douligeris and A. Mitrokotsa, “DDoS attacks and defense mechanisms: classification and state-of-the-art,” Comput. networks, vol. 44, no. 5, pp. 643–666, 2004, doi: https://doi.org/10.1016/j.comnet.2003.10.003.
L. Saldana, “The stages of implementation completion for evidence-based practice: protocol for a mixed methods study,” Implement. Sci., vol. 9, no. 3, pp. 1–11, 2014, doi: https://doi.org/10.1186/1748-5908-9-43.
R. Jabbar et al., “Blockchain technology for intelligent transportation systems: A systematic literature review,” IEEE Access, vol. 10, no. 5, pp. 20995–21031, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3149958.
A. Tandon, A. Dhir, A. K. M. N. Islam, and M. Mäntymäki, “Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda,” Comput. Ind., vol. 122, no. 11, p. 103290, 2020, doi: https://doi.org/10.1016/j.compind.2020.103290.
B. Cao et al., “Blockchain systems, technologies, and applications: A methodology perspective,” IEEE Commun. Surv. Tutorials, vol. 25, no. 1, pp. 353–385, 2022, doi: https://doi.org/10.1109/COMST.2022.3204702.
K. Saadat, “Flexible Blockchain Framework for Dynamic Cluster-based Applications,” University of Surrey, 2024.
P. Zappalà, M. Belotti, M. Potop-Butucaru, and S. Secci, “Game theoretical framework for analyzing blockchains robustness,” in 35th International Symposium on Distributed Computing (DISC 2021), Sorbonne Université, CNRS, Laboratoire d’Informatique de Paris 6, LIP6 …, 2021, pp. 41–42. doi: 10.4230/LIPIcs.DISC.2021.42.
Q. Wang, W. Li, and A. Mohajer, “Load-aware continuous-time optimization for multi-agent systems: Toward dynamic resource allocation and real-time adaptability,” Comput. Networks, vol. 250, no. 8, p. 110526, 2024, doi: https://doi.org/10.1016/j.comnet.2024.110526.
A. G. Barto, S. J. Bradtke, and S. P. Singh, “Learning to act using real-time dynamic programming,” Artif. Intell., vol. 72, no. 1–2, pp. 81–138, 1995, doi: https://doi.org/10.1016/0004-3702(94)00011-O.
V. Bala, E. Duesterwald, and S. Banerjia, “Dynamo: A transparent dynamic optimization system,” in Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation, 2000, pp. 1–12. doi: https://doi.org/10.1145/349299.349303.
Z. Liu et al., “A survey on blockchain: A game theoretical perspective,” IEEE Access, vol. 7, no. 7, pp. 47615–47643, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2909924.
A. Attiah, M. Chatterjee, and C. C. Zou, “A game theoretic approach to model cyber attack and defense strategies,” in 2018 IEEE International Conference on Communications (ICC), IEEE, 2018, pp. 1–7. doi: https://doi.org/10.1109/ICC.2018.8422719.
G. Fan, H. Yu, L. Chen, and D. Liu, “A game theoretic method to model and evaluate attack-defense strategy in cloud computing,” in 2013 IEEE International Conference on Services Computing, IEEE, 2013, pp. 659–666. doi: https://doi.org/10.1109/SCC.2013.110.
Q. Wang, W. Tai, Y. Tang, M. Ni, and S. You, “A two-layer game theoretical attack-defense model for a false data injection attack against power systems,” Int. J. Electr. Power Energy Syst., vol. 104, no. 4, pp. 169–177, 2019, doi: https://doi.org/10.1016/j.ijepes.2018.07.007.
M. Mavrovouniotis, C. Li, and S. Yang, “A survey of swarm intelligence for dynamic optimization: Algorithms and applications,” Swarm Evol. Comput., vol. 33, no. 4, pp. 1–17, 2017, doi: https://doi.org/10.1016/j.swevo.2016.12.005.
T. T. Nguyen, S. Yang, and J. Branke, “Evolutionary dynamic optimization: A survey of the state of the art,” Swarm Evol. Comput., vol. 6, pp. 1–24, 2012, doi: https://doi.org/10.1016/j.swevo.2012.05.001.
V. Deshpande, H. Badis, and L. George, “Efficient topology control of blockchain peer to peer network based on SDN paradigm,” Peer-to-Peer Netw. Appl., vol. 15, no. 1, pp. 267–289, 2022, doi: https://doi.org/10.1007/s12083-021-01248-6.
G. Jayabalasamy, C. Pujol, and K. Latha Bhaskaran, “Application of Graph Theory for Blockchain Technologies,” Mathematics, vol. 12, no. 8, p. 1133, 2024, doi: https://doi.org/10.3390/math12081133.
M. Swan, “Blockchain economic networks: Economic network theory—Systemic risk and blockchain technology,” in Business Transformation through Blockchain: Volume I, Springer, 2019, pp. 3–45. doi: https://doi.org/10.1007/978-3-319-98911-2_1.
M. Pirani, A. Mitra, and S. Sundaram, “Graph-theoretic approaches for analyzing the resilience of distributed control systems: A tutorial and survey,” Automatica, vol. 157, no. 3, p. 111264, 2023, doi: https://doi.org/10.1016/j.automatica.2023.111264.
V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, and J. Pineau, “An introduction to deep reinforcement learning,” Found. Trends® Mach. Learn., vol. 11, no. 3–4, pp. 219–354, 2018, doi: http://dx.doi.org/10.1561/2200000071.
H. Dong, H. Dong, Z. Ding, S. Zhang, and T. Chang, Deep Reinforcement Learning. Springer, 2020. doi: https://doi.org/10.1007/978-981-15-4095-0.
V. Singh, S.-S. Chen, M. Singhania, B. Nanavati, and A. Gupta, “How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda,” Int. J. Inf. Manag. Data Insights, vol. 2, no. 2, p. 100094, 2022, doi: https://doi.org/10.1016/j.jjimei.2022.100094.
A. Kosba, A. Miller, E. Shi, Z. Wen, and C. Papamanthou, “Hawk: The blockchain model of cryptography and privacy-preserving smart contracts,” in 2016 IEEE symposium on security and privacy (SP), IEEE, 2016, pp. 839–858. doi: https://doi.org/10.1109/SP.2016.55.
G. Zheng et al., “DRN: A deep reinforcement learning framework for news recommendation,” in Proceedings of the 2018 world wide web conference, 2018, pp. 167–176. doi: https://doi.org/10.1145/3178876.3185994.
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