cover
Contact Name
Alfian Ma'arif
Contact Email
alfian_maarif@ieee.org
Phone
-
Journal Mail Official
alfian_maarif@ieee.org
Editorial Address
Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Control Systems and Optimization Letters
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Articles 131 Documents
AI-Driven Threat Intelligence on Blockchain Using Deep Learning for Decentralized Cyber Risk Prediction Zangana, Hewa Majeed; Beitollahi, Hakem; Muhamad, Sabat Salih; Mohammed, Aquil Mirza; Wani, Sharyar
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.262

Abstract

The increasing complexity of cyber threats such as advanced persistent threats (APTs), ransomware, distributed denial-of-service (DDoS), and smart contract exploits requires cybersecurity solutions that go beyond traditional centralized defenses. This paper proposes an AI-driven threat intelligence framework integrated with blockchain technology for decentralized and trustworthy cyber risk prediction. The novelty of the proposed framework lies in its hybrid architecture, where deep learning–based anomaly detection models (including LSTM and autoencoder networks) analyze real-time cybersecurity data—such as blockchain transaction logs, network activity records, and external threat intelligence feeds—while blockchain is used to securely store, validate, and share AI-generated threat intelligence in a tamper-resistant and decentralized manner. Unlike AI-only solutions that suffer from data integrity and trust issues, or blockchain-only approaches that lack intelligent threat detection, the proposed framework combines the strengths of both technologies to enhance detection accuracy and stakeholder trust. Experimental evaluation conducted in a simulated blockchain environment demonstrates a detection accuracy of 96.4%, a false positive rate of 3.6%, and effective identification of multiple attack categories, including smart contract exploits and 51% attacks. While the framework improves security and transparency for inter-organizational security teams, enterprise networks, and supply-chain partners, it also introduces challenges related to computational overhead and blockchain scalability. Overall, the results indicate that integrating AI-driven threat intelligence with blockchain offers a practical and robust solution for decentralized cybersecurity risk prediction.