Institution profile

Prince Sattam bin Abdulaziz University

Academic institutionasia · sa
Official website
Research library9linked papers
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Selected work

Representative Papers

Belief-Space Quantum-Inspired Reinforcement Learning for Partially Observable Autonomous Cyber Defense in the Internet of Vehicles

Jun 05, 2026

This study addresses the limitations of static intrusion detection in vehicular networks, where adaptive adversaries and partial observability of the environment undermine defense efficacy. To tackle this challenge, the authors formulate security defense as a partially observable sequential attack-defense game and introduce a quantum-inspired, non-Bayesian amplitude-state belief representation mechanism. This approach effectively captures the defender’s uncertainty regarding attacker intent and integrates it into a Proximal Policy Optimization (PPO) framework to enable cost-aware dynamic defense decisions. Experimental results in a simulated environment demonstrate that the proposed method reduces cumulative average damage, damage variance, and attack success rate by 60.4%, 90.2%, and 50.0%, respectively, while improving system survival probability by 46.4% compared to baseline approaches, thereby significantly enhancing defensive effectiveness and robustness under partial observability.

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Quantum-Inspired Reinforcement Learning for Low-Latency Intrusion Detection in V2X and Internet-of-Vehicles Networks

Jun 05, 2026

This study addresses the limitations of traditional static defenses in vehicular networks, which fail to counter dynamic, multi-stage cyberattacks due to insufficient adaptability and high response latency. To overcome these challenges, the authors propose the QIRL framework, which uniquely integrates amplitude-phase quantum state encoding, a rotation gate-based exploration mechanism, and quantum interference-enhanced reward shaping into vehicular network security. The approach combines a lightweight deep Q-network with a cost-sensitive Markov decision process to model temporal attack dependencies and incorporates SMOTE oversampling to mitigate class imbalance. Evaluated on the CICIDS2017 and UNSW-NB15 datasets, QIRL achieves accuracy rates of 97.89% and 91.04%, F1 scores of 95.22% and 91.66%, and AUC-ROC values of 0.9945 and 0.9713, respectively, with per-sample inference latencies of only 32.5 and 45.7 microseconds—over 50 times faster than baseline methods.

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Transformer-Based Model for Multilingual Hope Speech Detection

Jan 31, 2026

This study addresses hope speech detection in English and German contexts, aiming to enhance the automatic identification of positive expressions in multilingual settings. For the first time, we apply RoBERTa (monolingual) and XLM-RoBERTa (English–German bilingual) to this task, leveraging their Transformer-based architectures and fine-tuning strategies for effective detection. Experimental results demonstrate that the proposed approach achieves an accuracy of 81.8% and a weighted F1-score of 0.818 on the English dataset, and 78.5% accuracy with a weighted F1-score of 0.786 in the multilingual English–German setting. These findings confirm the efficacy of pretrained language models in recognizing positively valenced text and highlight their strong potential for cross-lingual transfer in affective computing tasks.

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Recent publications

Latest Papers

Belief-Space Quantum-Inspired Reinforcement Learning for Partially Observable Autonomous Cyber Defense in the Internet of Vehicles

Jun 05, 2026

This study addresses the limitations of static intrusion detection in vehicular networks, where adaptive adversaries and partial observability of the environment undermine defense efficacy. To tackle this challenge, the authors formulate security defense as a partially observable sequential attack-defense game and introduce a quantum-inspired, non-Bayesian amplitude-state belief representation mechanism. This approach effectively captures the defender’s uncertainty regarding attacker intent and integrates it into a Proximal Policy Optimization (PPO) framework to enable cost-aware dynamic defense decisions. Experimental results in a simulated environment demonstrate that the proposed method reduces cumulative average damage, damage variance, and attack success rate by 60.4%, 90.2%, and 50.0%, respectively, while improving system survival probability by 46.4% compared to baseline approaches, thereby significantly enhancing defensive effectiveness and robustness under partial observability.

0 citationsRead paper

Quantum-Inspired Reinforcement Learning for Low-Latency Intrusion Detection in V2X and Internet-of-Vehicles Networks

Jun 05, 2026

This study addresses the limitations of traditional static defenses in vehicular networks, which fail to counter dynamic, multi-stage cyberattacks due to insufficient adaptability and high response latency. To overcome these challenges, the authors propose the QIRL framework, which uniquely integrates amplitude-phase quantum state encoding, a rotation gate-based exploration mechanism, and quantum interference-enhanced reward shaping into vehicular network security. The approach combines a lightweight deep Q-network with a cost-sensitive Markov decision process to model temporal attack dependencies and incorporates SMOTE oversampling to mitigate class imbalance. Evaluated on the CICIDS2017 and UNSW-NB15 datasets, QIRL achieves accuracy rates of 97.89% and 91.04%, F1 scores of 95.22% and 91.66%, and AUC-ROC values of 0.9945 and 0.9713, respectively, with per-sample inference latencies of only 32.5 and 45.7 microseconds—over 50 times faster than baseline methods.

0 citationsRead paper

Transformer-Based Model for Multilingual Hope Speech Detection

Jan 31, 2026

This study addresses hope speech detection in English and German contexts, aiming to enhance the automatic identification of positive expressions in multilingual settings. For the first time, we apply RoBERTa (monolingual) and XLM-RoBERTa (English–German bilingual) to this task, leveraging their Transformer-based architectures and fine-tuning strategies for effective detection. Experimental results demonstrate that the proposed approach achieves an accuracy of 81.8% and a weighted F1-score of 0.818 on the English dataset, and 78.5% accuracy with a weighted F1-score of 0.786 in the multilingual English–German setting. These findings confirm the efficacy of pretrained language models in recognizing positively valenced text and highlight their strong potential for cross-lingual transfer in affective computing tasks.

0 citationsRead paper