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.
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
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
Enhancing Internet of Things (IoT) Network Security: A Machine Learning-Driven Framework for Real-Time Intrusion Detection and Anomaly Classification
The rapid proliferation of IoT devices presents serious IoT network cybersecurity threats; hence, advanced IDSs are necessary. Signature and rule-based IDS mechanisms cannot address novel attacks, generate excessive alarms, and are computationally inefficient. Therefore, in response, in this paper, a machine learning IDS for IoT network real-time intrusion detection and anomaly categorization is proposed via black widow optimization (BWO) for optimal feature and hyperparameter selection. The IDS employs standard machine learning models, such as random forest and support vector machines (SVMs), and deep models, such as long short-term memory (LSTM), to address IoT environment nuances. The framework is evaluated on Bot-IoT and UNSW-NB15 datasets, such as various IoT-based attacks and normal traffic. The BWO algorithm maximizes feature reduction; for Bot-IoT, 57.1%; and for UNSW-NB15, 55.1%, while retaining better detection accuracy. Experimental evidence demonstrates the strength of the framework, where LSTM offers optimal detection accuracy (99.1%) and low false alarms (0.9%). The SVM model is computationally efficient and has a low training time (90 s), inference time (10 ms), space (200 MB) and power (40 joules). The framework's scalability is also an advantage, maintaining good precision despite expanding the dataset, and is therefore perfect for extensive IoT networks. The ability of BWO to rapidly converge ensures timely and efficient optimization, which is crucial for IoT applications in practice. The tradeoff between the capability to detect and the computational cost is achieved by the framework, overcoming the drawbacks of traditional IDSs and providing an efficient solution for IoT network protection. In conclusion, our solution innovates IoT security by using BWO and machine learning to ensure accurate detection, computational power, and scalability. The developed framework presents an efficient and effective solution for real-time intrusion detection, addressing the IoT's current and future needs for cybersecurity.
Copyright (c) 2025 Loiy Alsbatin, Firas Zawaideh, Basem Mohamad Alrifai, Tareq A. Alawneh https://creativecommons.org/licenses/by/4.0
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
A Novel of Bounded Zeghdoudi Distribution: Estimations, Simulation and Applications
In this article, we introduce a new unit Zeghoudi distribution, which is called the bounded Zeghoudi distribution (BZD), an innovative modification of the Zeghoudi distribution (ZD). This new suggested distribution retains the original ZD’s simplicity while improving modeling flexibility and accuracy for data constrained to the unit interval. The BZD displays numerous significant characteristics, including decreased, left-skewed, right-skewed, and unimodal probability density functions, but the hazard rate function can be J-shaped, U-shaped, and bathtub-shaped. Some important statistical features of the BZD, such as moments, mean, variance, moment generating function, lower and upper incomplete moments, mean residual life, mean inactivity time, some inequality measures, and order statistics, are computed. We demonstrate the effectiveness and reliability of the BZD using sixteen standard techniques for parameter estimation, supported by an extensive simulation analysis. Beyond its general statistical usefulness, the BZD is particularly relevant for cybersecurity analytics, where many key indicators such as intrusion detection rates, anomaly scores, attack probabilities, packet loss ratios, and vulnerability exploitability. The implementation of the BZD on four actual proportional datasets concerning failure rate, engineering, and medical data illustrates its efficacy and superiority compared to many well-known statistical models, such as ZD, unit Lindley distribution, the unit Teissier distribution, the reduced Kies distribution, exponentiated reduced Kies distribution, the Kumaraswamy distribution, beta distribution, and the unit Burr-III distribution. 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. See expression of concern available at: https://doi.org/10.58496/2026/021 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1045
Copyright (c) 2025 Mohammed Elgarhy, Hanene Hamdani, Laxmi Prasad Sapkota, Ahmed M. Gemeay https://creativecommons.org/licenses/by/4.0
A Multi-Factor Quantum-Resistant and Privacy-Preserving Authentication Protocol for Decentralized Systems
This paper presents a Quantum-Resistant and Privacy-Preserving Authentication Protocol (PPAP) designed for decentralized systems. PPAP integrates lattice-based cryptographic primitives, particularly Module-Lattice Key Encapsulation Mechanisms (ML-KEM), addressing both quantum computing threats and stringent privacy requirements. The protocol introduces advanced identity masking techniques and a multi-factor authentication layer employing Cheon-Kim-Kim-Song (CKKS) homomorphic encryption, enabling secure and privacy-aware authentication that supports approximate knowledge-based factors such as biometrics and handwritten signatures. Rigorous formal analysis, including cryptographic proofs and automated verification using the Tamarin Prover, validates PPAP's resilience against classical and quantum adversaries, as well as common security threats such as replay attacks, impersonation, and credential linkage. Performance evaluations demonstrate the practical feasibility of PPAP, highlighting its computational efficiency and minimized communication overhead, suitable for diverse scenarios ranging from Internet of Things (IoT) and mobile environments to high-security infrastructure. Furthermore, the Time-Aware Predictive Access Model (TAPM) significantly optimizes authentication lookup complexity, demonstrating substantial empirical improvement. Future work will focus on further performance enhancements, integration of additional biometric modalities, and deployment in practical decentralized applications.
Copyright (c) 2025 Ahmed Kh. Zamil, Ammar D. Jasim https://creativecommons.org/licenses/by/4.0
Arcsine Ratio Sine Generalized Distributions with Applications to Biomedical and Engineering Data
Trigonometric distributions offer a powerful approach to solving complex problems in probability and statistical modeling. In this study, we introduce a novel class of distributions: the Arcsine Ratio Sine Generalized (ARS-G) family. We provide a complete set of explicit formulas for the family’s statistical properties. Our key illustration is the ARS-Weibull (ARS-W) distribution, which uses the Weibull model as its foundation. This particular model demonstrates remarkable flexibility, with its hazard function capable of assuming diverse shapes, including bump, bathtub, reversed bathtub, J-shaped, and L-shaped profiles. We thoroughly examine the ARSW distribution’s statistical properties and employ various established estimation methods to determine its parameters. Through rigorous Monte Carlo simulations, we confirm the consistency and stability of these estimation methods. We then showcase the ARS-W model’s practical value by applying it to three real-world lifetime datasets: guinea pig survival times, active repair times for a communication transceiver, and turbocharger failure times. The model not only provides robust parameter estimates and a strong goodness-of-fit for the right-skewed guinea pig and transceiver data, but also outperforms traditional distributions (Weibull, Gumbel, Gamma, and Lognormal) for the left-skewed turbocharger dataset 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/10.58496/2026/020 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1044
Copyright (c) 2025 Kizito E. Anyiam, Mohamed A. F. Elbarkawy, Ehab M. Almetwally, Okechukwu J. Obulezi, Mohammed Elgarhy https://creativecommons.org/licenses/by/4.0
A Proposed Algorithm For Avoiding Jammer In Structure-Free Wireless Sensor Networks
Wireless Sensor Networks (WSNs) have emerged as a transformative technology with applications in critical and often inaccessible environments, including military and security domains. These networks comprise cooperative sensor nodes that gather and relay data to a base station. However, their inherent resource constraints—particularly the non-rechargeable nature of energy—pose a major challenge to network longevity and reliability. Conventional fixed-path transmission protocols exacerbate this issue, as energy-depleted or jammed nodes can disrupt communication, leading to partial or complete data loss. The primary objective of this study is to design and evaluate a structure-free transmission protocol that dynamically adapts data routing in order to optimize energy utilization and enhance resilience against jamming attacks. To achieve this, our study examines the impact of four distinct jammer types—Constant, Deceptive, Random, and Reactive—on key performance indicators, including energy consumption, signal-to-jamming ratio, and data rate. Simulation results reveal that jamming increases the number of transmission levels by up to 55%, with Deceptive Jammers generating the fewest and Constant Jammers the most. Energy consumption rises by as much as 62% under jamming, with Random Jammers causing the highest drain. Moreover, data rates increase by approximately 37% in the presence of jamming. These findings highlight the proposed protocol’s effectiveness in mitigating jamming effects while preserving network energy, offering a robust solution for WSNs deployed in hostile or high-risk environments.
Signal-To-Jamming Ratio ) SJR) And Attack.
Copyright (c) 2025 Zainab Shaker Matar Al-Husseini , Hussain K. Chaiel , Amel Meddeb , Ahmed Fakhfakh https://creativecommons.org/licenses/by/4.0
Mitigating Zero-Day Vulnerabilities in IIoT Systems: Challenges and Advances in AI-Powered Intrusion Detection Systems
Zero-day attacks are one of the great challenges that intrusion detection systems have been facing and keep on facing today, especially worsening within Industrial Internet of Things environments since their ability to take advantage of unknown vulnerabilities results in a high rate of false negatives. It is under this framework that this paper presents a set of experiments that have been carried out with the objective of analyzing the consequences of zero-day attacks with regard to performance degradation in Intrusion Detection System (IDS) and, secondly, and with greater emphasis, about those failings which have been pointed out up to now as those affecting precision in detection. This has been done through the systematic review of 200 research papers published from the years 2023 to 2024, further categorized into the four main focus areas: general AI-based IDS, Machine learning (ML)-based IDS, Deep Learning (DL)-based IDS, and Deep Reinforcement Learning (DRL)-based IDS. Accordingly, 45% were DL-based IDS reviews; 35% related to machine learning; 15% consisted of the ones about DRL-based ones, while 5% pertain to the General AI-based ones. Results show that the approaches with DL-based systems will come up with extensive promises, reducing the impact brought by false negatives, besides extending the issues even when one considers a background of adversarial attack issues. It underlines that, in IDS, apart from accuracy, detection specificity and recall are also of essence for dealing with low frequent but high-impact zero-day threats. These techniques further make the following proposal: the use of both machine learning and deep learning techniques should be improved in enhancing the performance of IDS.
Copyright (c) 2025 Khalid Asaad Hashim, Yusnani Binti Mohd Yussoff1, Shahrani Binti Shahbudin https://creativecommons.org/licenses/by/4.0
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
Blockchain-Based Accountability: A Systematic Literature Review
The Blockchain algorithm has advanced the accountability and transparency of modern digital infrastructures. Enforcing responsible behavior and data integrity across distributed environments involves several key components, such as smart contracts, access control models, cryptographic techniques, and a decentralized identity framework. Because the blockchain ledger is immutable and transparent, once a transaction is recorded, it cannot be altered without detection, making fraudulent actions easily traceable and thereby ensuring accountability. However, the need for hybrid approaches that combine on-chain and off-chain solutions for an efficient reliability system introduces challenges, including privacy preservation, scalability, and regulatory compliance. This paper analyzes the effective features that enhance blockchain accountability, such as immutability, traceability, auditability, and decentralized control. We propose research gap directions for the research community. To improve the reliability of blockchain systems across various domains, based on a systematic analysis and integration of recent developments and real-world demands. Consequently, we have distinguished 33 relevant research studies from a total of 358 publications covering the period between 2020 and 2025 by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. We identified three major themes addressed by the papers in the reviewed studies: further investigations into the ML role in enhancing accountability are required, especially using lightweight ML algorithms such as BNN and Tseltin machine, examining the limitations of blockchain’s auditability for real-time applications and decision-making efficiency, and a practical study of mechanism scalability in trade-off cost-efficiency.
Copyright (c) 2025 Nabeel Z. Tawfeeq, Dujan B. Taha https://creativecommons.org/licenses/by/4.0
PRIFLEX: A Secure Federated Learning Framework for Evaluating Privacy Leakage and Defense in Cross-Modal Medical Data
Federated learning (FL) enables decentralized, privacy-preserving machine learning by training models across distributed data without sharing raw patient information. However, most FL frameworks focus on unimodal data and overlook critical challenges in multimodal healthcare settings, such as privacy risks, fairness disparities, and reduced model interpretability. We present PRIFLEX (Privacy-Resilient Integration Framework for Learning Exchange), a novel FL framework designed for secure integration of structured and unstructured medical data. PRIFLEX combines 12-lead electrocardiograms (ECG) from the PhysioNet PTB-XL dataset and clinical notes from the Medical Information Mart for Intensive Care IV (MIMIC-IV), supporting early, late, and hybrid data fusion strategies. To safeguard model updates, it evaluates standalone and hybrid defenses using Differential Privacy (DP) and Secure Aggregation (SA) against gradient leakage, model inversion, and membership inference attacks. Results show that early fusion improves the area under the Receiver Operating Characteristic curve (AUROC) by up to 6.2%, while hybrid DP+SA reduces attack success rates by up to 84% and improves fairness with manageable system overhead. PRIFLEX also quantifies interpretability loss using SHapley Additive exPlanations (SHAP) and gradient-based methods, highlighting the trade-off between privacy and transparency. Overall, PRIFLEX sets a new benchmark for building secure, fair, and explainable federated learning systems in healthcare. 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/10.58496/2026/016 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1040
Copyright (c) 2025 Mohammad Othman Nassar , Feras Fares AL-Mashagba https://creativecommons.org/licenses/by/4.0
Comprehensive study of Integrating Clustering and Adversarial Learning for Enhanced Recommender Systems: A Systematic Review of Hybrid Methodologies and Applications
Recommender systems (RSs) have become critical elements in modern instances of information and decision-support systems, resulting in a transformation of user experiences through highly personalized suggestions for an undeniably vast range of items. Although RSs have become commonplace, they continue to evolve, and their challenges, including sparsity, cold-start, scalability, and vulnerability to adversarial challenges remain. The use of clustering methods has proven highly effective in resolving these issues through the discovery and exploitation of latent user behaviour patterns, segmenting user groups that contribute more towards personalized and adaptable RSs. Additionally, adversarial learning has become a growing focus of study as a proposed solution for shielding and defending RSs, processes, and data from manipulation and attacks, resulting in greater resistance and trustworthiness. This study presents a systematic literature review (SLR) that explores the intersection of RSs, clustering methods, and adversarial learning. This paper synthesizes a critique of the latest hybrid recommendations, detailing motivations, challenges, directions for future study, and practical recommendations drawn from the examined studies. An SLR search of four academic databases, ScienceDirect (SD), IEEE Xplore (IEEE), Scopus, and Web of Science (WoS), delivered an initial yield of 843 studies; after filtration, 51 studies remained. All the retained articles were examined and characterized in terms of dataset details, techniques, frameworks, and performance. A significant gap in research has emerged regarding the overreliance on datasets from commercial and entertainment domains, with a notable scarcity of studies addressing critical domains such as healthcare, finance, and other critical fields where diverse data sources should invoke robust, secure, and trustworthy RSs recommendations. Future research is needed to develop adversarially robust RSs for high-stakes applications requiring stringent accuracy and safety standards. This review provides a rich critical examination of the literature to embolden the ideas and theories associated with clustering methods and adversarial learning working together within RSs. It offers concrete opportunities and directions for carrying out future work in developing next-generation secure, adaptive recommendation frameworks. These findings corroborate a change in perspective on designing systems that seek to develop an RSs that can withstand adversarial threats and promote the development of safer, fairer, and more reliable decision-support systems in a variety of domains.
Copyright (c) 2025 M. E. Alqaysi, Murtadha M. Hamad, Ahmed Subhi Abdalkafor https://creativecommons.org/licenses/by/4.0
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
AntDroidNet Cybersecurity Model: A Hybrid Integration of Ant Colony Optimization and Deep Neural Networks for Android Malware Detection
Malware detection is a vital problem, and efficient methods that can efficiently detect malware are needed. The increasing use of mobile computers makes malware detection a vital part of security in an era where smartphones have come to play a key role in many of our daily lives. Earlier approaches, however, suffer from high false positive rates; they are not scalable for larger databases, or they are not amenable to adapt well to novel zero-day malware. For these reasons, the demand for more sensitive and flexible detection models is high. In this study, we develop a hybrid mobile malware detection framework that leverages ant colony optimization (ACO) and deep neural networks (DNNs) to improve detection accuracy, reduce the rate of false positives, and make the model resilient to new malware. AntDroidNet is a novel ACO-enabled feature selection model that dynamically reduces the feature dimensionality by selecting single instances to include the most informative properties and avoid dimensionality. A DNN is consequently constructed to train the determined set of features, improving the identified classification performance and decreasing the number of instances with false discoveries. In this way, a self-optimizing feedback loop can iteratively improve the feature selection process given the performance of the DNN, leading to a dynamic and efficient detection model. Using the CICMalDroid2020 dataset, the proposed AntDroidNet model achieves a remarkable accuracy of 99.89% and an excellent false positive rate of only 0.13% and outperforms the classical machine learning algorithms in terms of accuracy and efficiency. AntDroidNet is a scalable and powerful mobile malware detection model that eclipses all state-of-the-art methods and shows important enhancements in efficiency and reliability. By prototyping whitelisting systems, this work opens new avenues in mobile security and lays the groundwork for future work on building real-time detection components and system components able to scale to the fast pace of evolution of mobile malware in new connected ecosystems. 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. See expression of concern available at: https://doi.org/10.58496/MJCS/2026/003 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1027
Copyright (c) 2025 Riyadh Rahef Nuiaa Al Ogaili, Osamah Adil Raheem, Mohamed H Ghaleb Abdkhaleq, Zaid Abdi Alkareem Alyasseri, Saif Ali Abd Alradha Alsaidi, Ali Hakem Alsaeedi, Yousif Raad Muhsen, Selvakumar Manickam https://creativecommons.org/licenses/by/4.0
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
Deepfake Detection in Video and Audio Clips: A Comprehensive Survey and Analysis
Deepfake (DF) technology has emerged as a major concern due to its potential for misuse, including privacy violations, misinformation, and threats to the integrity of digital media. While significant progress has been made in developing deep learning (DL) algorithms to detect DFs, effectively distinguishing between real and manipulated content remains a challenge due to the rapid evolution of DF generation techniques. This study aims to address two key issues: the need for a comprehensive review of current DF detection methods and the challenge of achieving high detection accuracy with low computational cost. We conducted a systematic literature review to evaluate various DF detection algorithms, focusing on their performance, computational efficiency, and robustness. The review covers methods such as Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, hybrid models, and specialized approaches like spectral and phonetic analysis. Our findings reveal that while some methods achieve high accuracy, up to 94% in controlled environments, they often struggle to generalize across diverse DF applications. Hybrid models that combine CNNs and LSTMs typically offer a better balance between accuracy and computational efficiency. This paper provides valuable insights into the current state of DF detection and highlights the need for adaptive models that can effectively address the evolving challenges of DF generation.
Copyright (c) 2024 Wurood A. Jbara , Noor Al-Huda K. Hussein , Jamila H. Soud https://creativecommons.org/licenses/by/4.0
Dental segmentation via enhanced YOLOv8 and image processing techniques
By blending computer-aided medical systems with cutting-edge privacy technologies, healthcare providers can deliver more personalized, effective care while maintaining the highest data security standards and patient trust. The challenge of dental segmentation in computer vision, a task focused on accurately outlining dental structures in images, traditional methods, particularly convolution neural networks (CNNs), didn't reach high accuracy in this area due to suboptimal performance and computational inefficiency. The goal of image segmentation is to group pixels on the basis of their visual properties, such as color, texture, intensity, or spatial proximity, to identify and delineate the boundaries of distinct objects or regions within the image. In this paper, You Only Look Once (YOLOv8) algorithm is improved to segment teeth with high accuracy and high execution speed. The increase in the number of layers of YOLOv8 relied upon, as the accuracy of the algorithm segmentation depends on the number of layers used to extract features from the image (backbone) and the number of layers of the head (prediction). In addition, the size of the layers is decreased to increase the execution speed. The novelty of this paper is the proposed YOLOv8 model in addition to the Proposed Activation Function (PAF). The dataset (top view) used was taken from a dental clinic where 526 images were taken of dental and different patients. The best accuracy reached 99.561% when the enhanced YOLOv8 segmentation model was applied to the dental dataset. It can be concluded that the improved model of the YOLOv8 algorithm has increased the accuracy of dental segmentation compared to previous research because it relies on a proposed PAF that increases the difference between the features extracted from the layers of the proposed model which makes it able to distinguish between teeth and surrounding parts significantly.
Copyright (c) 2024 Dhiaa Mohammed Abed , Shuzlina Abdul-Rahman , Sofianita Mutalib https://creativecommons.org/licenses/by/4.0
AI-Powered Cyber Threats: A Systematic Review
The joining of artificial intelligence (AI) across different areas has fundamentally improved productivity and development. Nevertheless, this progression has increased cybersecurity threats, especially those determined by AI itself. These AI-powered threats exploit the advancements intended to obtain computerized frameworks, in this manner subverting their honesty. This systematic review focuses on the intricacies of AI-driven cyber threats, which use complex AI abilities to lead to intricate and tricky cyberattacks. Our review integrates existing examinations to determine the extension, location procedures, effects, and relief systems connected with AI-initiated threats. We feature the powerful exchange between AI improvement and cybersecurity, underlining the requirement for cutting edge protective frameworks that advance pairs with increasing threats. The discoveries highlight the basic job of AI in both carrying out and countering cybersecurity measures, representing a dualistic effect that requires ceaseless development in cybersecurity techniques.
Copyright (c) 2024 Mafaz Alanezi, Ruah Mouad Alyas AL-Azzawi https://creativecommons.org/licenses/by/4.0
Intermediary Decentralized Computing and Private Blockchain Mechanisms for Privacy Preservation in the Internet of Medical Things
Protecting patient data in the Internet of Medical Things (IoMT) is one of the major challenges facing healthcare organizations because of increasing threats to privacy and security. Although there are many existing protocols and solutions, such as Rivest–Shamir–Adleman (RSA) and El-Gamal cryptographies or centralized methods, that aim to protect data, they suffer from weaknesses such as slow performance or inability to handle large volumes of data. The issue of security in medical records has become an urgent need, and the use of centralized methods can expose them to single-point failure. In this paper, we present the efficient approach to securing patient information (EASPI), which depends on blockchain and integrates innovative techniques such as the advanced encryption algorithm (AES), reverse word frequency analysis (TF-IDF), Lemplel-Ziv-Welch (LZW), decision tree model (DTM), and naive Bayes classifier (NBC). EASPI seeks to improve the security of medical data by storing it encrypted and securely via blockchain technology, providing a high level of privacy and reliability. The experimental results indicate that the EASPI reduces the encryption execution time to 0.2 ms and the decryption execution time to 0.3 ms while improving the accuracy of medical diagnosis. The potential of the suggested methods for healthcare systems is further demonstrated by the fact that the TF-IDF algorithm attained an execution time of 0.004 ms, while the blockchain's greatest execution time was 0.014 ms. Additionally, using the formal verification Scyther tool, the security of the suggested system is examined both theoretically and practically. The suggested solution is an appropriate option for healthcare institutions since it offers a strong defense against a range of cyber threats, including targeted and espionage assaults.
Copyright (c) 2024 Rasha Halim Razzaq, Mishall Al-Zubaidie, Rajaa Ghali Atiyah https://creativecommons.org/licenses/by/4.0
Design and Practical Implementation of a Stream Cipher Algorithm Based on a Lorenz System
Currently, the security of data has gained significant attention in modern life. Researchers have continued to address this issue. This work addresses image encryption in communication systems. It presents a proposed design and implementation of a cryptography system based on the Lorenz chaos oscillator. The paper methodology uses Xilinx System Generator (XSG) and Field Programmable Gate Array (FPGA) technologies to implement the chaotic system. To determine the approach that uses the least amount of FPGA resources while providing effective and efficient performance, the differential equations of the Lorenz chaotic system are solved via the forward-Euler and Runge–Kutta integration techniques. In the XSG environment, a secure communication system is constructed on the basis of the solution of the differential equations. After that, the planned communication system is implemented on the FPGA board and tested to encrypt images (coloured images). The histogram, entropy and other related security analysis factors are calculated and analysed to test the efficiency of the designed system. Six statistical methods were employed to provide a high level of image encryption in this work. Findings have shown that the proposed system generates (with stable, fast and robust performance) pseudorandom bits that can be successfully used to encrypt the data bits. The simulation and FPGA results are in good agreement; however, the security analysis factors prove that the system can be successfully adopted for image encryption purposes in real-time applications.
Copyright (c) 2024 Hayder Mazin Makki Alibraheemi , Mazen M. A. Al Ibraheemi , Zainb Hassan Radhy https://creativecommons.org/licenses/by/4.0
A Systematic Literature Review on Cyber Attack Detection in Software-Define Networking (SDN)
The increasing complexity and sophistication of cyberattacks pose significant challenges to traditional network security tools. Software-defined networking (SDN) has emerged as a promising solution because of its centralized management and adaptability. However, cyber-attack detection in SDN settings remains a vital issue. The current literature lacks comprehensive assessment of SDN cyber-attack detection methods including preparation techniques, benefits and types of attacks analysed in datasets. This gap hinders the understanding of the strengths and weaknesses of various detection approaches. This systematic literature review aims to examine SDN cyberattack detection, identify strengths, weaknesses, and gaps in existing techniques, and suggest future research directions in this critical area. A systematic approach was used to review and analyse various SDN cyberattack detection techniques from 2017--2024. A comprehensive assessment was conducted to address these research gaps and provide a comprehensive understanding of different detection methods. The study classified attacks on SDN planes, analysed detection datasets, discussed feature selection methods, evaluated approaches such as entropy, machine learning (ML), deep learning (DL), and federated learning (FL), and assessed metrics for evaluating defense mechanisms against cyberattacks. The review emphasized the importance of developing SDN-specific datasets and using advanced feature selection algorithms. It also provides valuable insights into the state-of-the-art techniques for detecting cyber-attacks in SDN and outlines a roadmap for future research in this critical area. This study identified research gaps and emphasized the importance of further exploration in specific areas to increase cybersecurity in SDN environments.
Copyright (c) 2024 Dalia Shihab Ahmed, Abbas Abdulazeez Abdulhameed , Methaq T. Gaata https://creativecommons.org/licenses/by/4.0
QIS-Box: Pioneering Ultralightweight S-Box Generation with Quantum Inspiration
This paper presents the quantum-inspired substitution box (QIS-Box) algorithm, a novel approach aimed at enhancing the security of cryptographic S-boxes by leveraging quantum-inspired techniques. The QIS-Box algorithm significantly improves key cryptographic metrics, increasing nonlinearity from 102--110 and reducing differential uniformity from 6--4. Nonlinearity, which measures an S-Box's resistance to linear cryptanalysis, is enhanced to provide stronger protection, whereas differential uniformity, which assesses resilience to differential cryptanalysis, is improved to offer greater defence. These advancements highlight the robust security capabilities of the QIS-Box algorithm against prevalent cryptanalytic attacks. Furthermore, the algorithm demonstrates notable efficiency, making it well suited for implementation in resource-constrained environments such as IoT devices. This research contributes substantially to the development of quantum-resistant cryptographic solutions, addressing the challenges posed by emerging quantum computing technologies. Future research will focus on refining the simulation of quantum events within classical computational frameworks and integrating the QIS-Box Algorithm with other cryptographic techniques to further increase security and efficiency. This study paves the way for the development of advanced cryptographic systems capable of withstanding the evolving landscape of digital threats.
Copyright (c) 2024 Ghada Alkateb https://creativecommons.org/licenses/by/4.0
Memristive-Based Physical Unclonable Function Design of Authentication Architectures: A Systematic Review
Physically unclonable functions (PUFs) are advanced physical security measures that offer fundamental, unclonable appraisals of physical objects, providing an effective defense against hardware vulnerability breaches. They function as unique digital hardware fingerprints. This study discusses previous methods adopted for improving hardware security via PUF technology, with a specific focus on PUF circuits implemented on FPGA boards. Hardware security is assumed to be enhanced by adding a memristor to the ring oscillator PUF circuit and implementing these authentication architectures on FPGA boards. Additionally, this study explores methods for improving the main performance metrics for FPGA-based memristive-ring oscillator PUFs, including uniqueness, uniformity, and reliability. The study was founded on many scientific studies selected according to specific criteria. This study aims to assess and contrast these studies to achieve substantial enhancements in the security of devices on the basis of the obtained results. Upon comparing the findings, it was revealed that the proposed techniques, which provide flexibility and adaptability in dealing with memristive-PUF circuits to improve security services, displayed a distinct enhancement in security performance compared with other research that did not include any references to memristors. As an essential part of the authentication architecture, performance metrics involving memristor technology are verified in this study, with a uniqueness of 48.57%, uniformity of 51.43%, and bit-aliasing of 51.43%. These outcomes demonstrate the validation of memristor-based physical unclonable functions (M-PUF) against encryption and verification within a certified key exchange and tests.
Copyright (c) 2024 Hussien Al-Ani, Israa Al-Mashhadani https://creativecommons.org/licenses/by/4.0
Transaction Security and Management of Blockchain-Based Smart Contracts in E-Banking-Employing Microsegmentation and Yellow Saddle Goatfish
The process security of money transactions is considered an important issue in e-banking. Additionally, it is an enormous problem if never controlled. particular, security should be a combination of fast and sturdy characteristics, which is the subject of previous studies suffered from. Our research attempts to improve the system in which banks deal with the security of financial transactions. This research leverages the idea of microsegmenting the entire system into designated zones to concentrate on security, where each zone has its own rules and limitations. These rules are managed by a smart contract, which decides whether they have been observed to verify the legitimacy of the customer. First, the two-phase commit algorithm (2PC) was used to specify the type of e-banking request. After this, the microsegmentation principle was applied to isolate each type of e-transaction process alone in a separate segment. Then, the yellow saddle goatfish algorithm (YSGA) was used to determine whether the smart contract conditions were optimized. Finally, if the customer is authorized, then the entire transaction process is saved in the blockchain's main ledger and secured by a unique hash. The blockchain application makes our system capable of dealing with large numbers of users in a decentralized manner. In addition, using a hash with each block prevents fraudulent transactions by adversaries. Our system has been examined against several recent well-known assaults/attacks, such as falsification, advanced persistent threat, bribery, spoofing, double spending, chosen text, race, and transaction replay attacks, and has proven to overcome them. In terms of the performance evaluation, we obtained an execution time of approximately 0.0056 nanoseconds, 3.75% complexity, and 1500 KB of memory and disk drive, which is considered low compared to that of state-of-the-art research. Thus, our proposed system is highly acceptable for banking sector applications.
Copyright (c) 2024 Mishall Al-Zubaidie, Wid https://creativecommons.org/licenses/by/4.0
Cybersecurity Awareness among Special Needs Students: The Role of Parental Control
The awareness of cybersecurity among special needs students is necessary to help them stay safe while using technology. Recently, a good amount of interest has been drawn toward understanding the concepts and awareness of cybersecurity, and institutions have made efforts to help introduce awareness campaigns to help students understand the concepts of cybersafety, particularly for special needs students. The prior literature has focused primarily on exploring students' preferences, readiness, and experiences with cybersecurity. However, little attention has been given to measuring the level of cybersecurity awareness among students with special needs. To bridge this knowledge gap, the present study conducted an online survey to analyse the level of cybersecurity awareness and parental control among secondary school students with special needs aged 13 to 19 years in Malaysia. The study revealed that special needs students have a moderate level of cybersecurity awareness, with no significant difference among genders or academic streams. However, age does play a role in the level of awareness. Students with better cybersecurity knowledge are more satisfied with their online activities. Educating and monitoring special needs students on cyberattacks, password management, and phishing is crucial. In addition, parental control was found to be reasonable for most parents of students with special needs. This study contributes new knowledge by emphasizing the importance of parental control as a moderating variable in explanatory studies. It also highlights the need for further research in this area to expand the understanding of the importance of cybersecurity and how it can be implemented in specific school environments.
Copyright (c) 2024 Hapini Awang, Nur Suhaili Mansor (Author); Mohamad Fadli Zolkipli; Sarkin Tudu Shehu Malami , Khuzairi Mohd Zaini, Yau Ti Dun (Author) https://creativecommons.org/licenses/by/4.0
Zero-Day Vulnerabilities: Unveiling the Threat Landscape in Network Security
Today's digital ecosystem is particularly vulnerable to attacks due to zero-day vulnerabilities. Attackers take advantage of these flaws, which software developers and security experts are unaware of, leading to catastrophic outcomes. Through an analysis of their lifetime, discovery approaches, exploitation methods, disclosure protocols, and patching strategies, this study tries to reveal the danger landscape of zero-day vulnerabilities. We examine the effects of zero-day vulnerabilities on network security and the difficulties that businesses confront by reviewing the relevant literature and discussing actual cases. We also investigate preventative measures and methods of defense, such as intrusion and anomaly detection systems and cutting-edge AI. We stress the need for responsible disclosure, prompt patching, and continued research to counter these difficult to detect dangers. In order to better understand zero-day vulnerabilities, implement countermeasures, and respond to them, this article is an invaluable resource for researchers, security experts, and organizations.
Copyright (c) 2022 Raheela zaib, Kai-Qing Zhou https://creativecommons.org/licenses/by/4.0
SQL injection is a type of security vulnerability that occurs in database-driven web applications where an attacker injects malicious code into the application to gain unauthorized access to sensitive information. This paper aims to provide a comprehensive and systematic review of the existing methods for preventing and detecting SQL injection attacks. The review covers a range of techniques, including input validation, parameterized queries, and intrusion detection systems, as well as the advantages and disadvantages of each method. The most common prevention techniques include input validation, parameterized queries, and stored procedures, while the most common detection techniques include intrusion detection systems (IDS), honeypots, and signature-based detection. The choice of method will depend on the specific requirements of the organization and the level of security required. Still, a combination of prevention and detection methods is likely to be the most effective way to secure web applications against SQL injection attacks. The paper concludes that SQL injection attacks continue to be a significant security threat to web applications, and it is essential for organizations to implement effective prevention and detection methods to secure their web applications against SQL injection attacks.
Copyright (c) 2023 Vugar Abdullayev, Dr. Alok Singh Chauhan https://creativecommons.org/licenses/by/4.0
Detection of False Data Injection Attack using Machine Learning approach
The "False Data Injection" (FDI) attack is one of the significant security risks that the deep neural Network is susceptible to. The purpose of the FDI attacks is to deceive industrial platforms by faking sensor readings. considered a few relevant systematic reviews that have been previously published. Recent systematic reviews may include both older and more recent works on the topic. Therefore, I restricted myself to recently published works. Specifically, we analyzed data from 2016-2021 for this work. Attacks using FDI have effectively beaten out traditional threat detection strategies. In this paper, we provide an innovative auto-encoder-based technique for FDI attack detection (AEs). use of the temporal and spatial correlation of sensor data, which may be used to spot fake data. Additionally, the fabricated data are denoised using AEs. Performance testing demonstrates that our method is effective in finding FDI attacks. Additionally, it performs much better than a similar technique based on a support vector machine. The ability of the denoising AE data cleaning method to recover clean data from damaged (attacked) data is also shown to be quite strong.
Copyright (c) 2022 Siti Nur Fathin Najwa Binti Mustaffa, muhammad Farhan https://creativecommons.org/licenses/by/4.0