Journal of Cybersecurity in AI and Quantum Computing

ISSN: Pending Request (Online)Peer ReviewedOpen AccessOpen Access

Journal Information

Title
Journal of Cybersecurity in AI and Quantum Computing
ISSN
Request Pending
Type of Review
Review Speed
4 months
Digital Marketing
Online Submission

Journal Metrics

7

Articles

137

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0

Citations (Scopus)

0

Citations (Google Scholar)

0

Authors

0

Authors Distribution

40%

Acceptance Rate

Current Issue First Online

researchOpen AccessOpen AccessPublished: Oct 17, 2026

ONB-En 2.0: A Hardware-Optimized Lightweight Cipher with Bit-Sliced S-Boxes for IoT Security

Dena Abu Laila

Abstract.The deployment of the Internet of Things (IoT) has left a strong demand on lightweight cryptography. Security and efficiency algorithms on resource-constrained platforms. The given paper introduces the ONB-En 2.0, the improved variant of the ONB-En lightweight block cipher, which resolves the linearity issues that the predecessor was limited to by introducing the nonlinear elements in a very cautious manner. We develop a bit-sliced 4×4 S-box designing modelled by PRESENT, coupled with an optimized permutation layer that maintains hardware achievement while markedly improving resistance against differential and linear cryptanalysis. Our comprehensive security analysis demonstrates that ONB-En 2.0 achieves 10 rounds of security with differential probability bounded by 2−48 and linear bias magnitude below 2−24. Hardware implementation results on Xilinx Artix-7 FPGA show that ONB-En 2.0 requires only 1,247 gate equivalents (GE) with a throughput of 156.2 Mbps at 100 MHz, achieving 125 Kbps/GE efficiency. Comparative analysis against PRESENT, GIFT-128, and SPECK demonstrates that ONB-En 2.0 offers superior hardware efficiency while maintaining cryptographic strength suitable for IoT applications, including wireless sensor networks, RFID systems, and embedded secure communications.

researchOpen AccessOpen AccessPublished: Jan 1, 2026

A Conceptual AI-Assisted Cyber Risk Assessment and Management Framework for Critical Energy Infrastructure

Dawood alalisalem, Khalid albudair, M M Hafizur Rahman

Abstract.As technology advances, the digitalization of critical infrastructure has significantly increased cybersecurity risks across interconnected industrial ecosystems, particularly in the energy sector, where operational continuity and safety are essential. Traditional cyber risk assessment approaches are largely static, reactive, and incapable of effectively addressing large-scale, dynamic threat environments. To address these limitations, this study proposes a novel conceptual AI-assisted cyber risk assessment and management framework that integrates deep learning, machine learning, explainable artificial intelligence (XAI), and predictive analytics to enable adaptive, proactive, and real-time risk intelligence in energy distribution systems and smart grid networks. The novelty of the proposed framework lies in its integration of risk prediction, governance alignment, and automated mitigation into a unified intelligent architecture. Furthermore, the framework aims to improve threat prioritization, support resilient decision-making, and reduce response latency in critical energy infrastructure. By bridging artificial intelligence technologies with strategic cyber risk governance, this study introduces a forward-looking model designed to strengthen cybersecurity resilience in the evolving energy landscape.