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All Journal The Asian Journal of Technology Management (AJTM) Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi TIJAB (The International Journal of Applied Business) Journal of Information Technology and Computer Engineering Building of Informatics, Technology and Science Buletin Ilmiah Sarjana Teknik Elektro International Journal of Artificial Intelligence and Robotics (IJAIR) International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Sistem Pendukung Keputusan dengan Aplikasi Indonesian Journal of Education and Social Science Jurnal Ilmiah Computer Science The Indonesian Journal of Computer Science Control Systems and Optimization Letters
Zangana, Hewa Majeed
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Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : Control Systems and Optimization Letters

 1 
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.
A Hybrid Quantum-Classical Optimization Model for Reconfigurable Intelligent Surfaces in 6G Networks Zangana, Hewa Majeed; Sulaiman, Maryam A.
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.276

Abstract

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key enabler for sixth-generation (6G) wireless networks by providing programmable control over the radio propagation environment. However, optimizing RIS configurations in large-scale and dynamic 6G scenarios remains a computationally intensive and non-convex problem, particularly under realistic channel conditions involving user mobility, multi-user interference, and fading effects. This paper proposes a hybrid quantum–classical optimization framework that integrates a Variational Quantum Eigensolver (VQE)–based optimization module with classical iterative solvers to efficiently configure RIS phase shifts and reflection coefficients. The quantum component facilitates probabilistic exploration of the high-dimensional and combinatorial search space associated with large RIS deployments, while the classical component enforces system constraints and ensures convergence stability. Simulation results under realistic 6G channel models demonstrate that the proposed hybrid approach achieves up to 32% faster convergence, 18–25% improvement in spectral efficiency, and notable energy efficiency gains compared to state-of-the-art classical optimization techniques. Furthermore, the framework exhibits scalable performance with increasing RIS element counts and user density, highlighting its suitability for near real-time RIS control under noisy intermediate-scale quantum (NISQ) hardware constraints. These findings indicate that hybrid quantum–classical optimization constitutes a practical and scalable solution for intelligent, adaptive, and energy-efficient RIS-assisted 6G networks.
Co-Authors Abdulazeez, Adnan M Abdullah , Dilovan Taha Abdullah, Dilovan Taha Abualigah, Laith Al-Karaki, Jamal N. Ali , Natheer Yaseen Ali, Natheer Yaseen Amaliah, Fithrotul Irda Anastasia Lidya Maukar Anik Vega Vitianingsih As’ad Shidqy Aziz Ayaz khalid Mohammed B, Nur Vidia Laksmi Bazeed, Sameer Mohammed Salih Beitollahi, Hakem Cao, Xinwei DWI CAHYONO Firmansyah, Deden Hassan, Noor Salah Husain, Mamo Muhamad Li, Shuai Lidya Maukar, Anastasia Ma'rifani Fitri Arisa Mahmood Mustafa, Firas Mirza, Mohammed Aquil Mohammed , Ayaz Khalid Mohammed, Aquil Mirza Mohammed, Ayaz Khalid Mohammed, Harman Salih Muhamad, Sabat Salih Mustafa , Firas Mahmood Mustafa, Firas Mahmood Natheer Yaseen Ali Obeyd, Salah Ramadan Oliva, Diego Omar , Marwan Omar, Marwan Omar, Naaman Rizal, Moch Arif Samsul Salih, Azar Abid Sallow , Zina Bibo Sallow, Amira Bibo Sallow, Zina Bibo Salsabilah, Azka Slamet Riyadi, Slamet Riyadi Sulaiman, Maryam A. Wani, Sharyar Widi Aribowo Zeebaree, Subhi R. M.