Attention cybersecurity scholars and researchers! The Mesopotamian Journal of Cybersecurity has successfully launched and is rapidly gaining recognition in the academic community. With three impactful issues already published, our journal is attracting attention from respected publishers such as Scopus, Taylor & Francis, and others. We invite you to contribute your cutting-edge research and insights to our esteemed publication.
Blockchain-Integrated Edge-Cloud-Enabled Healthcare Data Analytics Based on Distributed Federated Learning and Deep Neural Networks
The exponential growth of healthcare Internet of Things (IoT) data necessitates secure, low-latency analytics that extend beyond centralized architectures. This paper presents BDAFL DNN, a blockchain-integrated data analytics framework that combines Federated Learning (FL) and Deep Neural Networks (DNNs) for real-time, privacy-preserving healthcare analytics across edge and cloud resources. Local devices such as smartwatches and phones collect noninvasive time series sensor streams (heart rate, temperature, and abdomen sensors), perform on device DNN training, and send only model updates to healthcare edge nodes, where a blockchain ledger validates updates for integrity and traceability; validated updates are then aggregated in the cloud via FL to produce a global model without sharing raw data. In a simulation study against representative baselines, BDAFL DNN reduced execution time, energy use, and resource consumption, lowered the deadline miss ratio, and improved blockchain validation correctness. These results show that integrating blockchain with FL-driven edge and cloud DNN analytics can deliver scalable, secure, and timely insights for future healthcare IoT systems. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Blockchain-Powered Dynamic Segmentation in Personal Health Record”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance. See expression of concern available at: https://doi.org/10.58496/2026/017 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1041
Copyright (c) 2025 Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Israa Badr Al-Mashhadani, Sajida Memon, Abdullah Lakhan, Haydar Abdulameer Marhoon, Marwan Ali Albahar https://creativecommons.org/licenses/by/4.0
Expression of Concern: Enhancing Intrusion Detection Systems with Adaptive Neuro-Fuzzy Inference Systems
EXPRESSION OF CONCERN FOR: Jitender Sharma, Sonia, Karan Kumar, Pankaj Jain, Raed H. C. Alfilh, Hussein Alkattan. (2025). Enhancing Intrusion Detection Systems with Adaptive Neuro-Fuzzy Inference Systems. Mesopotamian Journal of CyberSecurity, 5(1), 1-10. https://doi.org/10.58496/MJCS/2025/001 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/695 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Enhancing Intrusion Detection Systems with Adaptive Neuro-Fuzzy Inference Systems”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Expression of Concern: Healthcare Security in Edge-Fog-Cloud Environment using Blockchain: A Systematic Review
EXPRESSION OF CONCERN FOR: Zaid J. Al-Araji, Mahmood S. AlKhaldee, Ammar Awad Mutlag, Zaid Ali Abdulkadhim, Hussein M. Farhood, Sharifah Sakinah Syed Ahmad, Namaa N. Hikmat, Ayad Yassen, Abdullah A. Ibrahim Al-Dulaimi, Najwa N. Hazem Al-Sheikh, Ammar Hashim Ali. (2025). Healthcare Security in Edge-Fog-Cloud Environment using Blockchain: A Systematic Review. Mesopotamian Journal of CyberSecurity, 5(1), 606-635. https://doi.org/10.58496/MJCS/2025/037 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/844 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Healthcare Security in Edge-Fog-Cloud Environment using Blockchain: A Systematic Review”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Global Research Trends and Collaboration in MITRE ATT&CK Framework: A Bibliometric and Network Analysis in Cybersecurity
The MITRE ATT&CK framework has become a foundational tool for organizing adversarial behaviors and techniques in cybersecurity. While its adoption in practice and academia has grown significantly, no prior bibliometric review has comprehensively mapped the global research landscape surrounding this framework. This study addresses that gap by conducting a systematic bibliometric and network analysis of publications related to ATT&CK indexed in Scopus from 2017 to 2025. Using VOSviewer and quantitative bibliometric methods, we analyzed 391 publications to identify trends in research output, influential works, key contributors, thematic areas, and patterns of collaboration. Results show exponential growth in studies related to ATT&CK, with North America, Europe, and Asia as major contributors. Network analysis revealed highly interconnected author clusters, while keyword mapping identified five dominant research themes, including threat intelligence, adversary emulation, and machine learning-based detection. Citation analysis further identified inspiring publications that have significantly influenced the field. This review clarifies the intellectual structure and collaborative dynamics of ATT&CK research, offering insights into its development and pointing to future opportunities in interdisciplinary cybersecurity research. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Blockchain-Powered Dynamic Segmentation in Personal Health Record”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance. See expression of concern available at: https://doi.org/10.58496/2026/019 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1043
Copyright (c) 2025 Asad Maqsood, Syifak Izhar Hisham, Ahmad Firdaus https://creativecommons.org/licenses/by/4.0
Expression of Concern: A Novel of Bounded Zeghdoudi Distribution: Estimations, Simulation and Applications
EXPRESSION OF CONCERN FOR: Mohammed Elgarhy, Hanene Hamdani, Laxmi Prasad Sapkota, Ahmed M. Gemeay. (2025). A Novel of Bounded Zeghdoudi Distribution: Estimations, Simulation and Applications. Mesopotamian Journal of CyberSecurity, 5(1), 1292-1323. https://doi.org/10.58496/MJCS/2025/067 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/990 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “A Novel of Bounded Zeghdoudi Distribution: Estimations, Simulation and Applications”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Arcsine Ratio Sine Generalized Distributions with Applications to Biomedical and Engineering Data
EXPRESSION OF CONCERN FOR: Kizito E. Anyiam, Mohamed A. F. Elbarkawy, Ehab M. Almetwally, Okechukwu J. Obulezi, Mohammed Elgarhy. (2025). Arcsine Ratio Sine Generalized Distributions with Applications to Biomedical and Engineering Data. Mesopotamian Journal of CyberSecurity, 5(1), 1218-1271. https://doi.org/10.58496/MJCS/2025/065 https://journals.mesopotamian.press/index.php/CyberSecurity/article/view/985 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Arcsine Ratio Sine Generalized Distributions with Applications to Biomedical and Engineering Data”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network
Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to deception by morphed images. Finally, morph detection and classification are conducted using the proposed SNN framework, which incorporates a novel feature fusion strategy based on Canonical Correlation Analysis (CCA) to enhance discriminative power. The model is trained and evaluated using publicly available Face Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) datasets, comprising 1,030 real and 2,000 morphed images. Experimental results demonstrate that the proposed method significantly strengthens the resilience of FRS to morphing attacks, achieving a high detection accuracy of 99.9%. This confirms the model’s effectiveness in distinguishing between real and manipulated images with minimal errors. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Blockchain-Powered Dynamic Segmentation in Personal Health Record”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance. See expression of concern available at: https://doi.org/10.58496/2026/018 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1042
Faster Region-based Convolutional Neural Networks (R-CNN)
Local Binary Pattern-Convolutional Neural Network (LBP-CNN)
Copyright (c) 2025 Sura Abed Sarab Hussien , Thair Abed Sarab Hussien, Nada Hussien Mohammed Ali https://creativecommons.org/licenses/by/4.0
Expression of Concern: Global Research Trends and Collaboration in MITRE ATT&CK Framework: A Bibliometric and Network Analysis in Cybersecurity
EXPRESSION OF CONCERN FOR: Asad Maqsood, Syifak Izhar Hisham, Ahmad Firdaus. (2025). Global Research Trends and Collaboration in MITRE ATT&CK Framework: A Bibliometric and Network Analysis in Cybersecurity. Mesopotamian Journal of CyberSecurity, 5(1), 1165-1183. https://doi.org/10.58496/MJCS/2025/062 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/934 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Global Research Trends and Collaboration in MITRE ATT&CK Framework: A Bibliometric and Network Analysis in Cybersecurity”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network
EXPRESSION OF CONCERN FOR: Sura Abed Sarab Hussien, Thair Abed Sarab Hussien, Nada Hussein M. Ali (2025). Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network, 5(1), 1141-1164. https://doi.org/10.58496/MJCS/2025/061 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/933 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Blockchain-Integrated Edge-Cloud-Enabled Healthcare Data Analytics Based on Distributed Federated Learning and Deep Neural Networks
EXPRESSION OF CONCERN FOR: Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Israa Badr Al-Mashhadani, Sajida Memon, Abdullah Lakhan, Haydar Abdulameer Marhoon, Marwan Ali Albahar. (2025). Blockchain-Integrated Edge-Cloud-Enabled Healthcare Data Analytics Based on Distributed Federated Learning and Deep Neural Networks. Mesopotamian Journal of CyberSecurity, 5(1), 1122-1140. https://doi.org/10.58496/MJCS/2025/060 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/932 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Blockchain-Integrated Edge-Cloud-Enabled Healthcare Data Analytics Based on Distributed Federated Learning and Deep Neural Networks”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern : PRIFLEX: A Secure Federated Learning Framework for Evaluating Privacy Leakage and Defense in Cross-Modal Medical Data
EXPRESSION OF CONCERN FOR: Mohammad Othman Nassar, Feras Fares AL-Mashagba. (2025). PRIFLEX: A Secure Federated Learning Framework for Evaluating Privacy Leakage and Defense in Cross-Modal Medical Data. Mesopotamian Journal of CyberSecurity, 5(1), 1057-1080. https://doi.org/10.58496/MJCS/2025/057 https://journals.mesopotamian.press/index.php/CyberSecurity/article/view/922 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “PRIFLEX: A Secure Federated Learning Framework for Evaluating Privacy Leakage and Defense in Cross-Modal Medical Data”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern : Blockchain-Powered Dynamic Segmentation in Personal Health Record
EXPRESSION OF CONCERN FOR: Mishall Al-Zubaidie, Wid Alaa Jebbar (2025). Blockchain-Powered Dynamic Segmentation in Personal Health Record, 5(1), 953-976. https://doi.org/10.58496/MJCS/2025/054 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/910 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Blockchain-Powered Dynamic Segmentation in Personal Health Record”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: A Novel Diffusion-Based Cryptographic Method for Cyber Security
EXPRESSION OF CONCERN FOR: Kamaran Adil Ibrahim, Basim Najim Al-Din Abed, Sura Abed Sarab Hussien. (2025). A Novel Diffusion-Based Cryptographic Method for Cyber Security, 5(1), 842-862. https://doi.org/10.58496/MJCS/2025/048 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/887 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “A Novel Diffusion-Based Cryptographic Method for Cyber Security”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: An Artificial Intelligence-based System for Detecting Meta Fake Profiles via Gradient Boosting and Multilayer Perception
EXPRESSION OF CONCERN FOR: Ruaa A. Al-Falluji, Marwan Ali Albahar, Ahmad Mousa Altamimi. (2025). An Artificial Intelligence-based System for Detecting Meta Fake Profiles via Gradient Boosting and Multilayer Perception. Mesopotamian Journal of CyberSecurity, 5(1), 804-821. https://doi.org/10.58496/MJCS/2025/046 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/883 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “An Artificial Intelligence- based System for Detecting Meta Fake Profiles via Gradient Boosting and Multilayer Perception”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Blockchain and Federated Learning in Edge-Fog-Cloud Computing Environments for Smart Logistics
EXPRESSION OF CONCERN FOR: Guma Ali, Adebo Thomas, Maad M. Mijwil, Kholoud Al-Mahzoum, Malik Sallam, Ayodeji Olalekan Salau, Ioannis Adamopoulos, Indu Bala, Aseed Yaseen Rashid Al-jubori. (2025). Blockchain and Federated Learning in Edge-Fog-Cloud Computing Environments for Smart Logistics. Mesopotamian Journal of CyberSecurity, 5(1), 735-769. https://doi.org/10.58496/MJCS/2025/044 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/865 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Blockchain and Federated Learning in Edge-Fog-Cloud Computing Environments for Smart Logistics”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: SDN-Cloud Incident Detection & Response with Segmented Federated Learning for the IoT
EXPRESSION OF CONCERN FOR: Anas Harchi, Hicham Toumi, Mohamed Talea. (2025). SDN-Cloud Incident Detection & Response with Segmented Federated Learning for the IoT, 5(1), 671-684. https://doi.org/10.58496/MJCS/2025/040 https://journals.mesopotamian.press/index.php/CyberSecurity/article/view/855 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “SDN-Cloud Incident Detection & Response with Segmented Federated Learning for the IoT”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: A Stacked Ensemble Classifier for Email Spam Detection via an Evolutionary Algorithm
EXPRESSION OF CONCERN FOR: Salam Al-augby, Hasanen Alyasiri, Fahad Ghalib Abdulkadhim, Zahraa Ch. Oleiwi. (2025). A Stacked Ensemble Classifier for Email Spam Detection via an Evolutionary Algorithm. Mesopotamian Journal of CyberSecurity, 5(1), 567-670. https://doi.org/10.58496/MJCS/2025/039 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/846 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “A Stacked Ensemble Classifier for Email Spam Detection via an Evolutionary Algorithm”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Enhancing User Authentication through the Implementation of the ForestPA Algorithm for Smart Healthcare Systems
EXPRESSION OF CONCERN FOR: Nurul Syafiqah Zaidi, Al-Fahim Mubarak Ali, Ahmad Firdaus, Adamu Abubakar Ibrahim, Ghassan Saleh AlDharhani, Mohd Faizal Ab Razak. (2025). Enhancing User Authentication through the Implementation of the ForestPA Algorithm for Smart Healthcare Systems. Mesopotamian Journal of CyberSecurity, 5(1), 577-589. https://doi.org/10.58496/MJCS/2025/035 https://journals.mesopotamian.press/index.php/CyberSecurity/article/view/837 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Enhancing User Authentication through the Implementation of the ForestPA Algorithm for Smart Healthcare Systems”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: An Effective Feature Optimization Model for Android Malware Detection
EXPRESSION OF CONCERN FOR: Hussein K. Almulla, Hussam J. Mohammed, Nathan Clarke, Ahmed Adnan Hadi, Mazin Abed Mohammed. (2025). An Effective Feature Optimization Model for Android Malware Detection. Mesopotamian Journal of CyberSecurity, 5(1), 563-576. https://doi.org/10.58496/MJCS/2025/034 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/836 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “An Effective Feature Optimization Model for Android Malware Detection”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Cybersecurity Risk Assessment for Identifying Threats, Vulnerabilities and Countermeasures in the IOT
EXPRESSION OF CONCERN FOR: Mohammed Amin Almaiah, Rami Shehab, Tayseer Alkhdour, Mansour Obeidat, Theyazn H.H. Aldhyani. (2025). Cybersecurity risk assessment for identifying threats, vulnerabilities and countermeasures in the IoT. Mesopotamian Journal of CyberSecurity, 5(1), 514-537. https://doi.org/10.58496/MJCS/2025/032 https://journals.mesopotamian.press/index.php/CyberSecurity/article/view/829 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Cybersecurity risk assessment for identifying threats, vulnerabilities and countermeasures in the IoT”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Enhancing Cybersecurity with Machine Learning: A Hybrid Approach for Anomaly Detection and Threat Prediction
In today's digital era, cybersecurity has become a principal concern because of the increasing frequency and advancement of cyber threats. This study explores machine learning models for detecting and predicting anomalies in cybersecurity datasets. The research evaluates models such as linear regression, decision tree, RF, gradient boosting, KNN, SVR, LSTM, and neural networks utilizing performance metrics such as accuracy, MAE and MSE. A hybrid model that integrates different learning strategies is additionally proposed to improve the predictive accuracy and strength. The results highlight the superiority of ensemble approaches, especially the hybrid model, in improving peculiarity detection capabilities. The comparative analysis demonstrates that traditional models struggle with nonlinear patterns, whereas hybrid approaches successfully relieve this limitation. Moreover, this study emphasizes the importance of temporal data analysis for proactive threat detection and response. By leveraging diverse machine learning methods, this research contributes to strengthening cybersecurity infrastructures, enabling early threat detection, and minimizing security breaches. These discoveries emphasize the importance of adopting a comprehensive machine learning system to support cybersecurity resilience. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Enhancing Cybersecurity with Machine Learning: A Hybrid Approach for Anomaly Detection and Threat Prediction”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance. See expression of concern available at: https://doi.org/10.58496/2026/005 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1030
Copyright (c) 2025 Adil M. Salman, Bashar Talib Al-Nuaimi, Alhumaima Ali Subhi, Hussein Alkattan, Raed H. C. Alfilh https://creativecommons.org/licenses/by/4.0
Smartphone Authentication Based on 3D Touch Sensor and Finger Locations on Touchscreens via Decision-Making Techniques
Smartphone authentication systems must balance security and user convenience, which is a persistent challenge in the digital realm. Traditional biometrics, such as fingerprints and facial recognition, face vulnerabilities to spoofing and environmental conditions, limiting reliability. This study introduces a novel approach by integrating three-dimensional (3D) touch sensors with finger location data for authentication. The goal is to develop a system that improves accuracy while minimizing false positives and negatives, leveraging touch pressure and spatial interaction as unique biometric identifiers. Data from 20 participants, including pressure levels, spatial coordinates, and timestamps, were analysed using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. The results showed that combining pressure sensitivity with spatial data significantly improved performance, achieving an F1- score of 0.83 and an accuracy of 83%. The system demonstrated balanced precision (0.84) and recall (0.83), effectively reducing false positives and negatives. Robustness was confirmed through cross-validation tests, which validated the consistency across datasets and real-time usability scenarios. This study establishes a foundation for secure, user-friendly smartphone authentication, highlighting the potential of 3D touch technology in addressing current biometric system limitations. This approach opens avenues for further research in mobile security, integrating multimodal biometric data with advanced machine learning techniques. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Smartphone Authentication Based on 3D Touch Sensor and Finger Locations on Touchscreens via Decision-Making Techniques”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance. See expression of concern available at: https://doi.org/10.58496/2026/004 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1028
Copyright (c) 2025 Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida, Maytham M Hammood https://creativecommons.org/licenses/by/4.0
Expression of Concern: Threat Detection Based on Explainable AI (XAI) and Hybrid Learning
EXPRESSION OF CONCERN FOR: Shatha J. Mohammed, Bashar M. Nema, (2025). Threat Detection Based on Explainable AI (XAI) and Hybrid Learning, 5(1), 477-490. https://doi.org/10.58496/MJCS/2025/029 https://journals.mesopotamian.press/index.php/CyberSecurity/article/view/816 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Threat Detection Based on Explainable AI (XAI) and Hybrid Learning”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Enhancing Cybersecurity with Machine Learning: A Hybrid Approach for AnomalyDetection and Threat Prediction
EXPRESSION OF CONCERN FOR: Adil M. Salman, Bashar Talib Al-Nuaimi, Alhumaima Ali Subhi, Hussein Alkattan, Raed H. C. Alfilh https://doi.org/10.58496/MJCS/2025/014 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/751 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “ Enhancing Cybersecurity with Machine Learning: A Hybrid Approach for Anomaly Detection and Threat Prediction”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Exploring the Impact of Blockchain Revolution on the Healthcare Ecosystem: A Critical Review
Blockchain technology is a type of distributed ledger that provides secure and efficient storage, management, and transmission of data over a decentralized network. With its ability to ensure transparency and immutability, blockchain is increasingly adopted across various sectors ranging from finance, healthcare, and logistics to education. In healthcare, blockchain technology is attracting attention because of its potential to fundamentally transform health ecosystems. The healthcare sector has significantly benefited from blockchain technology by enhancing data security and interoperability and reducing medical errors. In this context, a set of studies highlighted the importance of blockchain in the field of healthcare, enhancing trust and security in the exchange of data and preventing unauthorized access. The article also studies the meaning, structure, function, types, and areas of use of blockchain technology and discusses the distribution of medical products in supply chain management. This article concludes that blockchain technology is highly important for storing health records, enhancing patient privacy, protecting patient data, and allowing the secure sharing of these data with physicians and healthcare workers. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Enhancing Intrusion Detection Systems with Adaptive Neuro-Fuzzy Inference Systems”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance. See expression of concern available at: https://doi.org/10.58496/MJCS/2026/002 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/708
Copyright (c) 2025 Maad M. Mijwil, Mohammad Aljanabi , Mostafa Abotaleb , Ban Salman Shukur, Ali S. Abed Al Sailawi, Indu Bala, Kamal Kant Hiran, Ruchi Doshi, Klodian Dhoska https://creativecommons.org/licenses/by/4.0
Expression of Concern: Smartphone Authentication Based on 3D Touch Sensor and Finger Locations on Touchscreens via Decision-Making Techniques
EXPRESSION OF CONCERN FOR: Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida, Maytham M Hammood. (2025). Smartphone Authentication Based on 3D Touch Sensor and Finger Locations on Touchscreens via Decision-Making Techniques. Mesopotamian Journal of CyberSecurity, 5(1), 165-177. https://doi.org/10.58496/MJCS/2025/011 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/734 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Smartphone Authentication Based on 3D Touch Sensor and Finger Locations on Touchscreens via Decision-Making Techniques”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: AntDroidNet Cybersecurity Model: A Hybrid Integration of Ant Colony Optimization and Deep Neural Networks for Android Malware Detection
EXPRESSION OF CONCERN FOR: Riyadh Rahef Nuiaa AlOgaili, Osamah Adil Raheem, Mohamed H Ghaleb Abdkhaleq, Zaid Abdi Alkareem Alyasseri, Saif Ali Abd Alradha Alsaidi, Ali Hakem Alsaeedi, Yousif Raad Muhsen, Selvakumar Manickam. (2025). AntDroidNet Cybersecurity Model: A Hybrid Integration of Ant Colony Optimization and Deep Neural Networks for Android Malware Detection. Mesopotamian Journal of CyberSecurity, 5(1), 104-120. https://doi.org/10.58496/MJCS/2025/008 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/711 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “AntDroidNet Cybersecurity Model: A Hybrid Integration of Ant Colony Optimization and Deep Neural Networks for Android Malware Detection”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Exploring the Impact of Blockchain Revolution on the Healthcare Ecosystem: A Critical Review
EXPRESSION OF CONCERN FOR: Maad M. Mijwill, Mohammad Aljanabi, Mostafa Abotaleb, Ban Salman Shukur, Ali S. Abed Al Sailawi, Indu Bala, Kamal Kant Hiran, Ruchi Doshi, Klodian Dhoska. (2025). Exploring the Impact of Blockchain Revolution on the Healthcare Ecosystem: A Critical Review. Mesopotamian Journal of CyberSecurity, 5(1), 1 10. https://doi.org/10.58496/MJCS/2025/006 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/708 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Exploring the Impact of Blockchain Revolution on the Healthcare Ecosystem: A Critical Review”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Data Mining and Machine Learning-Based Healthcare Monitoring in Cloud-IoT
EXPRESSION OF CONCERN FOR: Amer , S. ., Hazim, R. ., & Kader, W. . (2025). Data Mining and Machine Learning-Based Healthcare Monitoring in Cloud-IoT. Mesopotamian Journal of CyberSecurity, 5(1), 39 61. https://doi.org/10.58496/MJCS/2025/004 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/705 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Data Mining and Machine Learning-Based Healthcare Monitoring in Cloud-IoT”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Enhancing Advanced Persistent Threat Detection with Federated Learning and Neural Networks for Secure Cloud Computer Environment
Rapid internet expansion and global cloud storage use have heightened the risk of stealthy, persistent, multi-stage Advanced Persistent Threats (APTs). Distributed and resource-limited cloud environments make identifying these stealthy and dynamic threats difficult for traditional Intrusion Detection Systems (IDSs). This paper present FedNN-APT, a Federated Learning (FL) and hybrid Neural Network (NN) APT detection system to overcome these difficulties. High detection accuracy and distributed, privacy-preserving training over several cloud devices are achieved by this approach. FedNN-APT integrates Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs) to learn temporal and spatial APT behavior features effectively. The framework trains local models on partitioned datasets using GRU-CNN, 1D-CNN, and GRU-Recurrent Neural Network (RNN) models, then selects the optimal model for federated aggregation. The final global model is collaboratively built while preserving data confidentiality. The system is evaluated using an APT Malware dataset consisting of 11,107 samples. Experimental findings reveal that the hybrid GRU-CNN model outperforms other models with an average accuracy of 0.9977, precision of 0.9989, recall of 1.00, and F1-score of 0.9988. The federated model has 0.99global accuracy across four clients. A comparative evaluation with current APT detection systems underscores the superiority of FedNN-APT, especially regarding detection accuracy and flexibility in resource-limited environments. Finally, in this paper introduced FedNN-APT, an advanced approach that combines federated learning and neural networks for the detection of APT attacks while enhancing data privacy via a cloud environment and the findings indicate that incorporating deep learning models into a federated learning framework offers a promising direction for future study in safe and scalable threat detection in cloud systems.
Copyright (c) 2025 Baydaa Flayyih Hasan , Wafaa Ayoub Kassara , Bushra Raad Zahi https://creativecommons.org/licenses/by/4.0