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Journal : International Journal of Basic and Applied Science

Dynamic optimization algorithms for enhancing blockchain network resilience against distributed attacks Riandari, Fristi; Afrisawati, Afrisawati; Afifa, Rizky Maulidya; Syahputra, Rian; Ginting, Ramadhanu
International Journal of Basic and Applied Science Vol. 13 No. 2 (2024): Sep: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i2.499

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.
Co-Authors Ade Chandra Hasibuan Afifa, Rizky Maulidya Alif Daffa K Aprianto, M. Sura Arridha Zikra Syah Aruan, Nita Aulia Avenia Manurung Ayu Agustina Azmi Khoirun Nisa Chitra, Latiffani Dalimunthe, Yulia Agustina Dani, Rika Diana Daulay, Rahma Dimas Mahyudin Eska, Juna Febri Dristyan Finkan Lady Anwar Frandika, Imam Fristi Riandari Ginting, Ramadhanu Harahap, Indra Ramadona Hasbi Galih Santoso Hasibuan, Dzakwan Marsandi Pratama Helmiah, Fauriatun Hidayat Lubis, Rahmat Hud, Sofana Bayor Hutapea, Tiofani Br Ihsan, Fuadi Aula Intan Febrianti Irianto Irianto Irianto Irianto Irianto Irianto Irianto Irianto Isma Auli Mahrani Jeperson Hutahaean Juliantika, Rozi Khairunnatsri, Irham M Abian Zufri M Ihsan M. Handika Mahyudin, Dimas Manurung, Avenia Mardalius Mardalius Muhammad Dimas Muhammad Safii Muhammad Wahi Butar-Butar Natasya, Tri Adetia Novica Irawati Nur Adlina Nurul Aini Oktaviani, Retno Parini Parini Persadanta, Marwarizmi Al Zizzul Pudan Anggi Nami Dalimunthe Putri Nabillah Yusri Sirait Rabella Tidiwana Situmeang Rahman, Zulkarnaen Rainah Rainah Rian Syahputra Rida Fahari Suhada Rika Maria Riski Afdhalis Syahreza Rizky Yustanto Rizqy Maulana, Rizqy Rolly Yesputra Ruri Ashari Dalimunthe Ryandini Dwi Agusti Sahren Sahren Sahren sahren, sahren Santoso, Hasbi Galih Siti Khalijah Tanjung Siti Nurhalimah Sitorus Pane, Siti Fatimah Sri Wahyuni Suci Ramadhani Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sulistianingsih, Indri Susiani, Tiara Tiara Susiani Umi Kalsum Uswatun Hasanah Wulan Dari, Wulan Yanti, Elvie Yohana Dela Vega Yustanto, Rizky Zdensyah, Zainul Zulfikar Sitorus