SHIFRA (E_ISSN: 3078-3186) is an academic journal dedicated to advancing research in cybersecurity. Published by Peninsula Publishing Press in Dubai, SHIFRA provides a platform for scholars, researchers, and industry experts to publish original research, reviews, and case studies on topics such as threat detection, data privacy, network security, cryptography, and cybersecurity policy. The journal aims to address the latest challenges in the digital world, promoting innovative solutions that enhance security in an increasingly interconnected global environment.
A Hybrid Deep Reinforcement Learning and Graph Neural Network Framework for Adaptive Intrusion Detection in IoT-Enabled Cyber-Physical Systems
The Internet of Things (IoT) has enabled the rapid development of Cyber Physical Systems (CPS), but this has also created serious security concerns, particularly in the area of identifying sophisticated and changing cyberattacks. Traditional intrusion detection systems (IDSs) are built on static models and handcrafted features that are unable to keep up with the constantly changing nature of network behavior and new threats. This study presents a hybrid intelligent architecture built on Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL) for context-aware and adaptive intrusion detection in order to address these limitations. In order to aid the GNN component in capturing complex spatial and relational patterns between connected nodes over time, the suggested method treats the network traffic as a dynamic graph made up of devices as nodes and interactions between them as edges. With a multi-objective reward that takes into account detection performance, false positive rate, and computation latency, a DRL agent is also presented as a meta-controller that dynamically adjusts detection policies in response to the current network states and environmental feedback. The framework is evaluated using three cutting-edge IoT intrusion detection datasets Edge-IIoTset, TON_IoT, and CICIoT2023 under a variety of traffic scenarios, including concept drift and zero-day exploits. The suggested method achieves a 97. 2% F1 score and a 1. 1% false positive rate, according to experimental data, which is significantly better than conventional machine learning, deep learning, GNN-only, and DRL-only baselines. When subjected to unidentified assaults, the end-to-end framework only experiences a 6. 2% drop in the F1-score, compared to 18. 3% for static GNNs, and it recovers from concept drift 4. 7 times faster than DRL-only techniques, all while running with a low inference latency (27. 6 ms per batch). These findings show how promising adaptive decision-making and integrated structural graph learning are for safeguarding the next generation of IoT-CPS systems.
Copyright (c) 2026 Kholood J. Moulood, Maath Farman https://creativecommons.org/licenses/by/4.0
Channel-Agnostic Containment of Scam Stores in SDN Networks
In recent years, fraudulent shopping websites have continued to pose a serious threat despite heightened attention. Existing, case-specific security ecosystems often lack real-time mitigation, overlooking the myriad social channels, the short-lived nature of attacks, and the open windows that expose mobile commerce and PC-centric vulnerabilities. In this paper, we propose an automated method for gathering and detecting Fraudulent E-Commerce Websites (FCWs) by examining URLs from the very internet fabric such attacks rely on being HTTP, HTTPS, HTTP3 protocols in an SDN network. We relied on an in-network web retrieval system and a feature-enhanced XGBoost classifier built on the BeyondPhish model, to identify and categorize FCWs. Our model achieved a detection rate of 97.28% and a false-negative rate of 2.7%. Finally, detected FCWs are blocked via SDN OpenFlow rules applied to their IPv4 and IPv6 addresses, effectively closing the exploitation window that previous approaches left open for an average of 15 days or more when legal processes were required. By leveraging real-time, in-network web retrieval, classification, and mitigation, we achieved a pipeline detection-to-restriction time of under 30 seconds while blocking fraudulent URLs across all channels, demonstrating strong potential for enhancing mobile-commerce and PC-centric security.
Copyright (c) 2026 Joel Yao Adedi, Hui Xu https://creativecommons.org/licenses/by/4.0
Adaptive Hybrid Intrusion Detection Model byGenetic Algorithm and Transformer with Dynamic Feature Selection on behalf of Performance Optimization
Conventional intrusion detection systems were found to possess significant vulnerabilities to dynamic environments in changing networks, particularly with regards to their inability to adapt with the dynamic nature of attacks as well as their high sensitivity to high-dimensional features. In this paper, both a dynamic genetic algorithm-based feature selection method and Transformer based detection are interrelated and make up an adaptive hybrid intrusion detection model. The procedure followed a periodical re-selection process, triggered by an event that occurred as a result of the degradation in the performance as well as identification of statistical drift, and thus evolve with the variations in the data distributions. Transformer-based Transforming core was used, which utilized multi-heading self attention (MHSA) to represent the high-order interaction between features, and a hybrid output layer (HOL) that combined anomaly detection and multi-class classification. The model was evaluated on three baseline methods in large-scale experiments on two benchmark data, CIC-IDS2017 and UNSW-NB15. The findings prove that the proposed model achieves F1 scores of 97.64% and 95.02% on CIC-IDS2017 and UNSW-NB15, which is significantly higher than the baseline models by 2.09% to 9.69. The active selection mechanism was dynamic resulting in an average reduction of the number of active features by 42.3, which consequently meant a similar reduction in the inference time and no deterioration of the detection accuracy. In the simulated changes of the data distribution, the adaptive component performance remained consistent above the F1-score of 96.5% throughout the rest of the testing. Based on the inference latency of 14.2 milliseconds per sample and a highest memory utilization of 4.2 GB to 4.8 GB, the model suggested was considered to be viable to be used in a real-time environment. The results showed that combining dynamic feature selection with Transformer-based detection was a good method for intrusion detection in non-stationary network environments, overcoming the major limitations of existing methods and preserving enough computational efficiency to allow the possibility of practical use.
Copyright (c) 2026 Ali Sami Sosa https://creativecommons.org/licenses/by/4.0
An Intelligent Cloud Computing Security Approach Using a Dual Hybrid Cipher System
Although the development of cloud computing has led to a paradigm shift in the data storage, it has also subjected the sensitive information to massive security and performance threats; that is, the data integrity and processing overhead. There is a basic trade-off in classical cryptography: the symmetric-key protocols are really fast, whereas they do not provide secure exchange of keys, but the asymmetric-key protocols are very secure and computationally infeasible when dealing with large amounts of data. Conventional cryptographic techniques commonly have a fundamental dilemma: symmetric-key protocols can offer fast execution but do not include any secure key-exchange protocols, whereas asymmetric-key systems can offer high protection but cannot be used with large volumes of data due to their slow computation speed. To alleviate these problems, this study presents a streamlined dual-cipher architecture that is aimed at boosting the efficiency of the cloud through the integration of the AES-256 and RSA-2048. The intelligence of the system lies in a dynamic workload distribution mechanism such that the high-resource encapsulation of the RSA operations is limited to the encryption of keys, while the bulk encryption is left to the high-speed AES engine. The empirical assessment of different file sizes (1 MB to 1 GB) shows that a sustainable throughput of 56-72 MB/s is achieved at 83-86 percent of the functional speed of standalone AES. As a result, the suggested model provides a viable and scalable design for modern cloud infrastructures with a very low overhead of just 20-21 percent or so relative to single deployments of RSA.
Copyright (c) 2026 Ayad Osama Jalal https://creativecommons.org/licenses/by/4.0
Human Factors in Information Security: A Quantitative Study on Phishing Susceptibility and Awareness Levels
The research focused on studying multiple aspects of phishing attack vulnerability and the corresponding knowledge about phishing by examining how psychological and social and environmental elements affect people in their phishing attack susceptibility. We analyzed data from 1,200 participants through a quantitative approach which involved a standardized questionnaire to evaluate their phishing attack susceptibility and their knowledge of phishing threats across different demographic segments. The data revealed that people between 18 and 25 years old became the most vulnerable age group to phishing attacks because their susceptibility rate reached 45% which exceeded the 30% rate found in older age groups (p 0.01). The test results showed that participants who had limited cybersecurity knowledge obtained phishing detection scores which were 15% lower than those who had better cybersecurity understanding (p 0.05).People who have restricted social connections network will click malicious links at a rate which is 40% higher than other users according to the analysis. The research showed that users who accessed their devices through mobile phones were 25% more likely to fall for phishing attacks than those who used their desktop computers for device access. Educational content requires customization because research findings show mobile platforms and population groups face different obstacles which need to be addressed. The research findings show that cybersecurity training programs which provide complete coverage should combine community-based training with language-based programs to enhance security knowledge. Scientists must keep their research on phishing methods because these attacks keep evolving while educators need to develop adaptable educational approaches which defend students from these common security threats. Research findings show how human factors interact with information security systems to develop improved protection methods which defend against phishing attacks.
Copyright (c) 2026 Abdulazeez Alsajri, Amani Steiti https://creativecommons.org/licenses/by/4.0
Behavioral Analysis of Modern Malware Traffic Using Statistical Network Features
The research focused on studying modern malware traffic behaviors through statistical network feature application to improve detection systems. The research team conducted a quantitative analysis of traffic data which included multiple malware family samples to study their operational system requirements and their need for system resources. We evaluated more than 10,000 traffic samples through statistical methods which revealed that benign processes and malicious processes needed different resources and showed distinct operational patterns. During their operations the malware samples demonstrated rising service needs while their resource usage reached the peak value of 75% throughout their most active periods. The Trojan-Zeus variant and other related malware samples needed dynamic link libraries (DLLs) to function because they averaged 15 DLLs per sample which exceeded the 8 DLLs used by normal processes. Our research found that malware which targets operational systems achieved better system access and stealth capabilities which made it more difficult for conventional detection systems to identify them. The study results show that malware development follows an emerging pattern which requires detection systems to adapt their methods based on the distinct features of each malware type. Our research indicates that cybersecurity professionals need to integrate machine learning algorithms with statistical analysis methods in their frameworks for effective identification and prevention of upcoming security threats. The research demonstrates that cybersecurity methods require continuous innovation because modern malware threats need advanced defense systems which enable systems to withstand complex cyber threats that continue to evolve.
Copyright (c) 2026 George Benneh Mensah, Alfred Addy https://creativecommons.org/licenses/by/4.0
Zero Trust Security: A Bibliometric Review of Concepts, Adoption, and Research Gaps
This bibliometric analysis examines the evolving landscape of Zero Trust Security literature, emphasizing the trends, key contributors, and research gaps within this critical field. Using the Biblioshiny R package, we retrieved a total of 1,200 publications from 2015 to 2023, reflecting a marked increase in scholarly output that underscores the growing recognition of Zero Trust frameworks in cybersecurity. Analysis revealed a consistent growth trend, peaking in 2021, with a notable decline in publication rates thereafter, suggesting potential saturation or shifting focus in research topics. The most productive authors included five leading researchers who contributed over 150 publications collectively, with prominent journals such as the Journal of Cybersecurity and IEEE Access being the primary platforms for dissemination. Co-citation analysis identified several thematic clusters, including the integration of artificial intelligence in Zero Trust strategies and the challenges of implementing these frameworks in diverse organizational contexts. Keyword clustering revealed emerging topics such as "resilient security" and "cyber threat adaptation," highlighting areas ripe for further exploration. Despite the increased volume of literature, significant gaps persist, particularly concerning empirical studies that assess the practical application of Zero Trust principles across various sectors. This analysis underscores the necessity for future research to explore these empirical dimensions and foster a more comprehensive understanding of Zero Trust Security, ultimately guiding practitioners in effective implementation strategies within an ever-evolving digital landscape.
Copyright (c) 2026 Kulondwa Mburunge, Khder Alakkari, Bushra Ali https://creativecommons.org/licenses/by/4.0
A Survey on Securing Smart Finance using Artificial Intelligence and Blockchain
The rapid digitalization of financial services has given rise to smart finance ecosystems that integrate FinTech platforms, Internet of Things (IoT) devices, cloud infrastructures, and decentralized applications. While these systems enhance automation, operational efficiency, and financial inclusion, their highly distributed, data-intensive architectures introduce critical security, privacy, and trust challenges. In this context, artificial intelligence (AI) and blockchain have emerged as complementary technologies capable of addressing these challenges through intelligent decision-making, advanced threat detection, data integrity, and transparent operations. This survey provides a comprehensive review of recent research on securing smart finance systems using AI- and blockchain-based approaches. The survey comprehensively analyzed research published between 2023 and 2026 using the Scopus database, focusing on the keywords “AI,” “blockchain,” and “smart finance.” The analysis reveals extensive use of AI-driven security mechanisms, including credit scoring and risk assessment, transaction monitoring and fraud detection, anti-money laundering (AML) and know-your-customer compliance, identity verification, cyber threat detection, smart contract security analysis, behavioral biometrics, insurance fraud detection, and market risk prediction. In parallel, the survey examines blockchain-enabled security solutions, including secure payment and settlement systems, cross-border remittances, AML and counter-terrorism financing frameworks, digital identity management, smart contracts, asset tokenization, decentralized finance, auditability, and secure interbank communication. The integration of AI and blockchain offers significant advantages, including improved fraud detection accuracy, enhanced transparency and traceability, stronger data integrity, automated compliance, real-time threat response, and increased system resilience. Despite these benefits, key challenges persist, particularly in scalability, privacy preservation, interoperability, regulatory and ethical compliance, energy efficiency, explainability, and post-quantum security. The survey concludes by outlining future research directions and design guidelines for developing secure, scalable, and trustworthy smart finance systems that effectively leverage the integration between AI and blockchain.
Copyright (c) 2026 Guma Ali, Otim Emmanuel, Maad M. Mijwil, Bosco Apparatus Buruga, Aida Vafae Eslahi, Ioannis Adamopoulos https://creativecommons.org/licenses/by/4.0
Attention-Based Deep Learning for Scenario Description Embedding in Cyber Threat Records
This study aimed to address the increasing sophistication of cybersecurity threats by analyzing a substantial dataset to evaluate the efficacy of detection methodologies. Utilizing a dataset comprising 14,133 records and 16 variables, we investigated the prevalence of various cyber threats and assessed the performance of machine learning models in their detection. Our methodological approach centered on the application of attention-based neural networks, which facilitated a comprehensive analysis of complex threat scenarios. Results showed that unauthorized access and data breaches were the most prevalent threats, representing a significant portion of the dataset. Additionally, malware and phishing attacks emerged as notable threats, highlighting the necessity for enhanced detection strategies. The implementation of attention-based neural networks significantly improved threat detection capabilities, with these models effectively classifying diverse threat patterns. This methodological advancement underscores the potential of machine learning in augmenting cybersecurity measures. Our findings suggest a pressing need for targeted detection methods tailored to specific threat categories, thereby optimizing threat detection and response strategies. The identification of system vulnerabilities emphasizes the importance of proactive mitigation strategies to bolster cybersecurity resilience. These results indicate that integrating advanced machine learning models into cybersecurity frameworks can substantially enhance detection accuracy and efficiency. In conclusion, this research provides critical insights into the landscape of cybersecurity threats and the effectiveness of detection methodologies. By leveraging a large dataset, we have elucidated key patterns in threat prevalence and detection efficacy. Our findings reinforce the imperative for ongoing innovation in cybersecurity practices, with attention-based neural networks offering a promising avenue for future exploration.
Copyright (c) 2025 Zakaria Benlalia, Toufik Mzili, Mustapha Hankar, Ahmed abatal, Mourad Mzili, Nebojsa Bacanin, Momina Shaheen https://creativecommons.org/licenses/by/4.0
Hypergraph-Based Time-Aware Modeling for Multi-Phase Cyber Threat Simulation and Anomaly Detection
This research uses a time-aware hypergraph design and anomaly-detection methods to simulate cyberattack sequences. Multi-phase cyber-attacks have high-order interactions, concurrency, and unpredictable timing that traditional directed graph models cannot describe. To overcome this constraint, we create hypergraphs with cyber kill-chain stages including reconnaissance, initial access, execution, persistence, privilege escalation, and exfiltration as nodes and hyperedges for logical and temporal groups. Randomized temporal information is added to each simulated phase to create a two-hour enemy timeline. Repetitive simulations introduce regulated phase length and sequence ordering diversity. Feature-engineering pipelines include temporal alignment, stage ordering, and label encoding. Scatter-based anomaly plots and frequency-distribution histograms show aberrant phase lengths from an Isolation Forest model. A transition-probability matrix quantifies hypergraph stage-to-stage movement and better models adversarial behavior. Structured datasets from simulations are provided for IDS training, red-team emulation, and threat-analysis investigations. The proposed time-aware hypergraph better reconstructs Advanced Persistent Threat (APT) behavior by capturing overlapping phases, higher-order relationships, and temporal uncertainty than DAG-based representations. The research compares the proposed hypergraph model to a baseline DAG model using the same synthetic dataset to demonstrate demonstrable changes in anomaly-detection performance and IDS-dataset quality to assure scientific validity. The findings section provides measurements and validation methodologies for the 22% anomaly-detection accuracy and 30% IDS-dataset quality increase over the DAG baseline.
Copyright (c) 2025 Ahmed Hussein Ali, Nadia Mahmood Hussien, Saba Abdulbaqi Salman, Yasmin Makki Mohialden, Kapil Joshi https://creativecommons.org/licenses/by/4.0
Blockchain and Quantum Machine Learning Approach for Securing Smart Water Management Systems: A Scoping Review
Smart water management systems (SWMS) increasingly rely on Internet of Things (IoT) devices to enhance water distribution, detect leaks, and support sustainable resource use, but this reliance also heightens exposure to cyberattacks, data manipulation, and privacy risks. Conventional security approaches often fall short due to the decentralized design and real-time demands of these systems. This scoping review analyzes 266 studies published between January 2022 and December 2025 to assess how integrating Blockchain and quantum machine learning (QML) can strengthen the security, privacy, and reliability of SWMS. The review examines Blockchain-enabled water management, quantum computing applications, and QML-based security frameworks, using thematic analysis to categorize emerging architectures and challenges. Findings of the focused studies show growing adoption of Blockchain for secure data logging, access control, and tamper-proof auditing. At the same time, QML demonstrates strong potential in anomaly detection, predictive maintenance, and optimizing distribution networks. Although these technologies offer a promising foundation for resilient water infrastructure, most research remains conceptual, with limited real-world deployment or scalability assessments. Integrating Blockchain with QML could create robust, privacy-preserving SWMS frameworks. However, significant barriers persist, including the computational intensity of quantum models, interoperability issues with existing IoT infrastructures, and the absence of standardized protocols. Addressing these gaps is essential for practical implementation. This review underscores the need for scalable hybrid designs, applied validation, and cross-disciplinary standards to advance secure, efficient, and sustainable smart water management solutions.
Copyright (c) 2025 Guma Ali, Maad M. Mijwil, Ioannis Adamopoulos, Klodian Dhoska https://creativecommons.org/licenses/by/4.0
The impact of artificial intelligence on enhancing cyber security
Due to the increasing number of connectivity devices, the IIoT has raised lot of security problems. The IoT networks need to be secure and efficient for the sustenance of such networks. We provide an amalgamation of FL and optimization methods for anomaly detection in IIoT. Integrating CNNs into the framework could boost the model accuracy and meanwhile reduce communication cost. Differential privacy mechanisms are adopted to provide data security and protect user privacy. The framework supports high accuracy, superior precision and recall and manages the training efficiently. The strongness and scalability of the proposed method provide a better proposition for addressing security issues in IIoT compared to central machine learning based approaches.
differential privacy homomorphic encryption
Copyright (c) 2025 Abdulqader Abdul-Latif Shahoud, Hadeel Msaleh https://creativecommons.org/licenses/by/4.0
An Intelligent Intrusion Detection Framework Using Deep Learning and Unsupervised Feature Selection for Industry 4.0
Manufacturing has been revolutionized by the integration of advanced technologies such as the Internet of Things, cyber-physical systems, and cloud computing, but it is also exposed to a variety of cyber-attacks because of these technologies. A system for detecting unsupervised intrusions is proposed as an answer to these growing security challenges, which we call the Unsupervised Intrusion Detection System for Industry 4.0. Using the isolation forest method, the framework identifies anomalous network traffic anomalies through random forest-based feature selection. Based on deep learning and unsupervised anomaly detection, the proposed system is more accurate, computationally efficient, and reduces false positives than traditional intrusion detection systems. With the proposed model, interconnected industrial systems will be protected from evolving cyber threats, demonstrating enhanced security for Industry 4.0 systems.
Copyright (c) 2025 Vugar Abdullayev , Nazila Ragimova https://creativecommons.org/licenses/by/4.0
Secure and Fault-Tolerant IoT-Based Healthcare System for Reliable Decision-Making
Internet of Things (IoT) integration has revolutionized healthcare, offering personalized medical care through remote patient monitoring and telemedicine. However, challenges remain in critical healthcare scenarios, such as data security vulnerabilities and system failures. This paper proposes an IoT-based healthcare system that is fault-tolerant and secure. As part of the proposed Fault-Tolerant Data Management (FTDM), advanced encryption techniques, fault-tolerant data management, and intelligent algorithms will safeguard patient data and guarantee system resilience. Utilizing a fault-tolerant system with two levels enhances data management, reduces energy consumption, and minimizes execution time. Based on simulations using iFogSim, the results indicate significant reductions in energy consumption, network usage, execution time, and fault tolerance compared with existing approaches. IoT-enabled healthcare systems can be deployed in resource-constrained environments with greater security and reliability based on the results of this study.
Copyright (c) 2025 Aparna Achanta , Bashaer Almelehy, Mohammad Ahmad , Ghalia Nassreddine https://creativecommons.org/licenses/by/4.0
An Integrated Federated Learning Framework with Optimization for Industrial IoT Intrusion Detection
Because of the rapidly growing number of connected devices, the Industrial Internet of Things (IIoT) has presented significant security challenges. IoT networks must be secure and efficient in order to remain sustainable. We present an integration of federated learning and optimization techniques to detect intrusions in IIoT. By combining CNNs with the proposed framework, model accuracy can be enhanced while communication costs are minimized. Data security and user privacy are ensured through the implementation of differential privacy mechanisms. In addition to high rates of accuracy, precision, and recall, the framework has an efficient training management process. With its robustness and scalability, the proposed method offers a better solution for IIoT security concerns than traditional centralized machine learning methods.
Copyright (c) 2025 Aitizaz Ali, Aseel AlShuaibi , Muhammad Waqas Arshad https://creativecommons.org/licenses/by/4.0
An Enhanced Hybrid Genetic-JAYA Algorithm for Feature Selection and SVM Parameter Optimization in Intrusion Detection Systems: Evaluation on the CICIDS Dataset
The changing scenario in the cybersecurity field requires intelligent and adaptive means of detecting sophisticated intrusions. This paper presents an improved hybrid Genetic-JAYA algorithm for simultaneous feature selection and hyperparameter tuning of an SVM-based IDS. Leveraging the CICIDS2017 dataset, which is well-known for its comprehensive representation of modern attack types, the model interprets the classification as a binary problem, distinguishing normal traffic from malicious activity. The presented hybridization employs the global exploration ability of the Genetic Algorithm (GA) with the parameter-less, convergence-oriented behavior of the JAYA algorithm. To select the subset of features, a custom fitness function is adopted that balances classification accuracy and feature compactness, while the hyperparameters of the SVM using a radial basis function (RBF) kernel are optimized concurrently for improved detection. All results were obtained using stratified 5-fold cross-validation, with metrics averaged over 10 independent runs. The hybrid GA-JAYA-SVM model outperformed GA-SVM and JAYA-SVM, achieving an average accuracy of 98.73% (±0.45), precision of 98.63%, recall of 98.79%, and a reduced false positive rate of 1.15%. The robustness and generalization capability of the model are further supported by AUC and F1-score measurements. Statistical evidence, via the Wilcoxon signed-rank test, validates the observed performance gains (p < 0.05). These results illustrate the capability of the proposed model to enhance IDS performance with minimal computational overhead an essential requirement for real-time deployment. Overall, the findings suggest that hybrid metaheuristic approaches, such as the Genetic-JAYA combination, offer strong potential for improving IDS effectiveness and form a promising foundation for future research into adaptive, intelligent, and layered cybersecurity defenses.
Copyright (c) 2025 Mohanad G. Yaseen, Ahmed Hussein Ali, Mohammad Alajanbi https://creativecommons.org/licenses/by/4.0
Beyond Detection: Large Language Models and Next-Generation Cybersecurity
The Integrating Contextual and Adaptive Cyber Defense Systems The accelerating evolution of cyber threats, seen both in terms of their complexity and frequency of attempts, makes it imperative to move away from traditional, reactive defense structures and towards intelligent, adaptive and proactive cyber defense strategies. Large Language Models (LLMs) (such as GPT this-will-be-a-zillionth-of-a-second-type-here or BERT derivatives) represent powerful new capabilities developed for understanding, analyzing, and generating human-like language with contextual depth enabling a new frontier in the land of cyber defense. This survey investigates the influence of LLMs on multiple key technical areas of cybersecurity including software and system security, network security, content moderation, hardware security, and blockchain security. It showcases applications of LLMs in tasks in vulnerability detection and prevention, phishing, malware detection and analysis, and intrusion detection; as well as smart contract auditing and adversarial attack simulation. The paper goes more in-depth on technical constructs like zero-shot and few-shot threat hunting, prompt engineering for scenario modeling, and retrieval-augmented generation for real-time intelligence. Task differences are captured in knowledge retrieval from external sources, enabling more contextual learning and task adaptability, inter-model interaction, and augmentation through domain-specific fine-tuning. While promising, LLMs pose open challenges, such as hallucination, adversarial misuse, generalization problems, and ethical issues around privacy and accountability. The implications of such comparative analysis across domains highlight the strengths as well as ‘emerging risk’ associated with LLM systems at mission critical environments. The survey ends with a highly visionary section on the future opportunities around future self-healing systems, autonomous cyber agents, and the future of LLMs that can not only detect threats but also predict, simulate, and respond automatically to threats. This work serves as a foundational roadmap for researchers and practitioners for the use of LLMs for the next generation of resilient cybersecurity tools.
Copyright (c) 2025 Aitizaz Ali, Mohamed C. Ghanem https://creativecommons.org/licenses/by/4.0
XAI-IDS: A Transparent and Interpretable Framework for Robust Cybersecurity Using Explainable Artificial Intelligence
The rapid evolution of cyberattacks, and in particular DDoS attacks, has outpaced many intrusion detection systems (IDSs) that fail to be interpretable or transparent, which subsequently hinders wider adoption in real-world high-stake environments. To our knowledge, this is the first joint utilization of CNNs with both SHAP and LIME for XAI-IDS tasks, particularly based on comparative evaluations across a wide range of different intrusions and a combination of XAI methods. The proposed method starts with data acquisition and data preprocessing of NSL-KDD dataset as only DDoS labeled records output are chosen and this is followed by data cleaning through data normalization, feature selection and label encoding to get accurate results. The dataset is divided into training and testing sets, a 1D CNN is trained on the dataset to differentiate DDoS attacks from normal traffic using the hyperparameters with optimized values and early stopping. It yields high predictive performance with 94% accuracy, 93% precision, 95% recall, and 94% F1-score. For these results, it demonstrates strong classification ability with very few false positive and false negative. Stack SHAP for global feature importance and LIME for individual predictions to give us human-understandable explainable results of the model. Not only does this dual explainability cultivate trust and accountability, it also enhances auditing and compliance in sensitive industries. Overall, XAI-IDS shows that the combination of deep learning and post-hoc interpretability is a promising approach to the design of trustworthy systems for cybersecurity. Future works will be conducted toward real-time deployment and multi-class detection in federated and edge learning frameworks.
Explainable Artificial Intelligence for Intrusion Detection Systems (XAI-IDS)
Local Interpretable Model-Agnostic Explanations (LIME)
Copyright (c) 2025 Said Salloum, Sajedeh Norozpour https://creativecommons.org/licenses/by/4.0
Generative AI-Enhanced Intrusion Detection Framework for Secure Healthcare Networks in MANETs
Recent developments in healthcare security and network intrusion detection have seen the domain of Artificial Intelligence (AI) act as a decisive presence due to its increased adaptability and more resilient level of resistance towards emerging cyber threats. Due to decentralized nature and its cost-effective communication, mobile ad hoc networks (MANETs) are widely used in healthcare applications and present security vulnerabilities like access from unauthorized nodes, node mobility and the bandwidth constraint. In general, traditional encryption and authentication alone will not eliminate these threats, and thus, advanced intrusion detection systems (IDS) based on deep learning (DL) are necessary. In this paper, we propose an AIIDS for secure healthcare networks in MANET using deep neural networks (DNN) to enhance the threat detection and alleviating the cyberattacks. In particular, the model is to integrate Cascading Back Propagation Neural Network (CBPNN), Feedforward Neural Network (FFNN), and Convolution Neural Network (CNN) in order to detect malicious activity, increase the detection accuracy and meet the robust security standards. Experimental results show that the average receiving packet (ARP) and end-to-end (E2E) delay of the proposed model is 74% (CBPNN), 82% (FFNN) and 85% (CNN) detection accuracies with 27s, 18s, and 17s of response time respectively. They suggest that incorporating AI in IDS can help enhance healthcare MANET environments’ security against emerging threats.
Copyright (c) 2025 Santosh Reddy Addula, Udit Mamodiya, Weiwei Jiang, Mohammed Amin Almaiah https://creativecommons.org/licenses/by/4.0
Federated Learning in Healthcare: A Bibliometric Analysis of Privacy, Security, and Adversarial Threats (2021-2024)
Federated Learning (FL) has rapidly emerged as a transformative machine learning approach, enabling healthcare institutions to collaboratively build predictive models without compromising patient data privacy. As healthcare increasingly adopts digital technologies, federated learning offers promising solutions to critical issues such as data privacy, security, data poisoning, and adversarial attacks. Despite the recognized potential of FL, significant gaps persist in existing research, particularly concerning comprehensive security frameworks and practical healthcare applications. This bibliometric analysis systematically explores the research landscape from 2021 to 2024, explicitly focusing on data privacy, security threats, and adversarial attacks within federated learning in healthcare. Utilizing bibliometric data from the Scopus database, the study identifies key thematic trends, evaluates global collaborative networks, and assesses contributions from leading institutions and countries. Findings reveal rapidly growing scholarly interest, robust international collaboration, and notable institutional contributions, with a specific emphasis on privacy-preserving techniques, healthcare-specific applications, and emerging technologies such as blockchain and edge computing. The analysis also highlights critical limitations due to incomplete bibliographic metadata. This research provides a comprehensive understanding of current trends and identifies future directions to enhance the security and privacy framework of federated learning in healthcare.
Copyright (c) 2025 Mohammed Amin Almaiah, Rejwan Bin Sulaiman, Umar Islam, Youakim Badr, Fuad Ali El-Qirem https://creativecommons.org/licenses/by/4.0
A Survey on Artificial Intelligence and Blockchain Applications in Cybersecurity for Smart Cities
Smart cities rapidly evolve into transformative ecosystems where advanced technologies work together to improve urban living. These interconnected environments use emerging technologies to offer efficient services and sustainable solutions for urban challenges. As these systems become more complex, their vulnerability to cybersecurity threats also increases. Integrating artificial intelligence (AI) and Blockchain technologies to address these challenges presents promising solutions that ensure secure and resilient infrastructures. This study provides a comprehensive survey of integrating AI, Blockchain, cybersecurity, and smart city technologies based on an analysis of peer-reviewed journals, conference proceedings, book chapters, and websites. Seven independent researchers reviewed relevant literature published between January 2021 and December 2024 using ACM Digital Library, Wiley Online Library, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, IGI Global, and Google Scholar. The study explores how AI can enhance threat detection, anomaly detection, and predictive analytics, enabling real-time responses to cyber threats. It examines various AI methodologies, including machine learning and deep learning, to identify vulnerabilities and prevent attacks. It discusses the role of Blockchain in securing data integrity, improving transparency, and providing decentralized control over sensitive information. Blockchain’s tamper-proof ledger and smart contract capabilities offer innovative solutions for identity management, secure transactions, and data sharing among smart city stakeholders. The study also highlights how combining AI and Blockchain can create robust cybersecurity frameworks, enhancing resilience against emerging threats. The survey concludes by outlining future research directions and offering recommendations for policymakers, urban planners, and cybersecurity professionals. This study identifies emerging trends and applications for enhancing the security and resilience of smart cities through innovative technological solutions. The survey provides valuable insights for researchers and practitioners who aim to utilize AI and Blockchain to improve smart city cybersecurity.
Copyright (c) 2025 Asiku Denis, Adebo Thomas, Wamusi Robert, Aziku Samuel, Simon Peter Kabiito, Zaward Morish, Malik Sallam, Guma Ali, Maad M. Mijwil https://creativecommons.org/licenses/by/4.0
Encryption of Color Images utilizing cascading 3D Chaotic Maps with S-Box Algorithms
The fundamental characteristics of chaos, including sensitivity to beginning conditions and unpredictability, render it a prime candidate for cryptographic applications. This research introduces an encryption methodology for effective and safe image encryption. The encryption system has two ciphering phases and a substitution phase. This study proposes a method for key creation. The design of a pseudo-random number generator utilized for key generation is founded on chaotic algorithms. The chaotic map will be employed in encryption systems owing to its superior security. NIST tests are employed to assess the randomness of the proposed PRNG sequences. The subsequent part presents a security study of the suggested picture encryption approach to evaluate its efficacy. The statistical analysis now confirms that the technique is secure and efficient for encrypting both basic and complicated images, whether in monochrome or color. Through a comparison with previous chaotic investigations, it is evident that our method is competitive with earlier efforts.
Copyright (c) 2023 Jenan Ayad, Guma Ali, Waheed Ullah, Wamusi Robert https://creativecommons.org/licenses/by/4.0
Directed Mining of the Dark Web Using Hell9: Advanced Techniques for Exploring and Analyzing Anonymous Networks
In the World Wide Web the dark net is still theoretically part of the Internet. But its access software or configuration and permission is different from the other sites. The dark web is not indexed by the public web search engines, such as Google and Bing. If you type in any keyword there will be pages of millions returned as a result. For example, trying to quantify the vastness of the Internet. If one were to think about how many sites are representative for just one hundred keywords, and how many since “n” different number combinations are equally valid on search engine queries? When we browse the Internet using the web browsers to visit sites our eyes really just see what is called the Surface Web. Which is a part of the Internet you can visit from different hypertext information displayed as webpages simply. Still, without the help of search engines, you can use the manifold resources from Internet that the Surface Web cannot present to you. The deep web where Tor is used is a place hard for hackers, spies, and even government agencies to watch over users. However when they are monitoring the site they cannot tell how website operators are processing with their data files on them all of a sudden. This paper describes the Businessman, Scientist, Students & Any Person safely and Anonymously Getting onto the DarkNet 100% with HeLL9 Project.
Copyright (c) 2024 Thaar Kh. Asman, Haider D. Abd https://creativecommons.org/licenses/by/4.0
Blockchain technology has been used in a variety of non-financial industries, including commerce, healthcare, and other fields. With the use of this technology, a decentralized environment free from the control of outside societies over data and transactions can be found. In essence, blockchain is a novel technology that has the potential to revolutionize future transaction-based transactions. Cross-border trade has traditionally been supported by financial tools. Blockchain technology has been implemented to develop trade finance processes. In the trade of tenants, anxiety reduction, payments, and financial solutions are essentially viewed as a storm to be optimized. This study looks at how the invention of Bitcoin has aided in the development of blockchain technology. The cryptocurrency is still in its infancy. Blockchain technology has been used to build an easy-to-use digital economy. The characteristics, benefits, and drawbacks of blockchain technology are examined thoroughly in this study article. It also examines how this technology is integrated with the development of trade finance and cryptocurrencies.
Copyright (c) 2024 Ahmed B.Abudul Kareem https://creativecommons.org/licenses/by/4.0
The Role of the Iraqi National Data Center in Advancing Digital Transformation and Data Sovereignty
This study examines the key role of the National Data Center in Iraq’s digital transformation, focusing on the Iraqi National Data Center (INDC) as a key infrastructure for centralized data management and the digital role You trust to play. Iraq’s current digital infrastructure is severely limited, including outdated technologies, cybersecurity weaknesses, regulatory gaps and economic constraints. These obstacles prevent INDC from meeting the increasing demands for data storage, resources and security required for effective government functioning and self-reliance in the digital age f Deliver systems centralized for By implementing this approach, INDC will be able to enhance its data governance and security processes, align with international data authority standards, and forge partnerships that support long-term growth. The findings of this study suggest that targeted reforms in infrastructure, cybersecurity, and regulatory frameworks can significantly increase the efficiency of the INDC government, protect national intelligence from external threats, and help Iraq broader digital transformation objectives And of getting continued investment , emphasizes the importance of skilled labor development and system exchange.
Copyright (c) 2024 Israa Ibraheem Al Barazanchi , Dima Haider Rasheed https://creativecommons.org/licenses/by/4.0
An Overview of Image Steganography Techniques: Historical Development, Methodologies, and Evaluation Criteria
This paper provides a detailed overview of image steganography, tracing its historical roots and examining the development of modern techniques. Steganography, derived from the Greek words "stegos" (cover) and "graphia" (writing), refers to the practice of concealing information within a host medium. Historically, steganographic techniques included tattooing hidden messages and using invisible ink, but the digital age has introduced new possibilities for secure data embedding, particularly within images. With the advent of the internet, the need for robust steganographic techniques has increased, leading to advancements in both spatial and frequency domain methods, including Least Significant Bit (LSB) replacement, Discrete Cosine Transform (DCT), and wavelet-based techniques. This paper also reviews evaluation criteria such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index Measure (SSIM) to assess the effectiveness of various steganographic methods in achieving high security, capacity, and imperceptibility. Challenges such as balancing these criteria, developing multi-criteria evaluation tools, and optimizing embedding processes are highlighted. This review aims to provide insights into current advancements, limitations, and future directions for research in image steganography.
Copyright (c) 2024 Omar Mejbel Hammad, Ikram Smaoui, Ahmed Fakhfakh, Mohammed Mahdi Hashim https://creativecommons.org/licenses/by/4.0
Recent advances in digital image masking techniques Future challenges and trends: a review
Image steganography has advanced over recent decades, driven by a demand for secure, high-capacity data-hiding techniques capable of maintaining imperceptibility in digital images. This study provides a comprehensive review of steganographic techniques, highlighting recent improvements across spatial and frequency domains and innovations that integrate cryptographic methods, genetic algorithms, and machine learning to enhance security and payload capacity. The assessment reveals trade-offs between imperceptibility, capacity, and security in various methods, including least significant bit (LSB) substitution, frequency domain transformation, edge detection, and pixel intensity manipulation. This analysis identifies key research gaps in multi-criteria evaluation, PSNR reliability, and the need for enhanced methodologies in imperceptibility and robustness to withstand statistical and steganalysis attacks. By investigative these trends, this research emphasizes the importance of complementary image quality and data safety to improve effective and resilient stenographic systems.
Copyright (c) 2024 Omar Mejbel Hammad , Mohammed Mahdi Hashim, Ahmed Fakhfakh, Ikram Smaoui https://creativecommons.org/licenses/by/4.0
Achieving Sustainability in Construction: The Role of Advanced Composite Materials
Because of its high resource and energy consumption and carbon emissions, the construction industry comes across as one of the most environmentally unfriendly industries, as traditional materials such as concrete, steel, and timber pose even greater reputational and performance risks, sustainability challenges, a climate crisis, resource extraction, sprawl, in addition to being the largest source of human-related carbon emissions. A drive toward more environmentally friendly construction methodologies has created an interest in advanced composite materials (ACMs) (including Fibre Reinforced Polymers (FRP), Natural Fibre Composites (NFC), Bio-composites and Nano-composites), which have been proven to possess many beneficial characteristics including high strength-to-weight ratios, great durability and potential to be very environmentally friendly. Despite these benefits, there has been little comparative analysis of ACMs versus traditional materials across the key sustainability metrics over the full life cycle. This study seeks to fill this knowledge gap by conducting a detailed assessment of ACM in terms of performance, environmental impact and sustainability throughout their life cycle. Specific aims include studying the mechanical properties of ACMs like the strength-to–weight ratio, durability, thermal insulation, recycle ability, and carbon footprint, and comparing them with traditional construction materials. The research applies a sequential process consisting of a literature review, characterization of materials, case studies and Life Cycle Impact Assessment (LCIA) of ACSMs to assess their potential to be viable alternatives for sustainable construction. The key contributions of this work are the in depth understanding of the relative performance of ACMs with respect to traditional materials, the identification of specific areas where ACMs are superior in sustainability metrics, and the identification of limitations in their recyclability and fire resistance that still require further R & D. These results endorse ACMs as viable alternatives to conventional materials, leading to less resource use, lower emission, and more sustainability in construction practice. This research offers practical civil engineering-oriented recommendations and establishes a basis for additional investigation on how to improve ACMs for wider, sustainable applications.
Copyright (c) 2024 Noor Al-Huda K. Hussein, Ryah Nughaimesh Sultan https://creativecommons.org/licenses/by/4.0
Optimizing Energy Efficiency in Smart Grids Using Machine Learning Algorithms: A Case Study in Electrical Engineering
The increasing demand for power driven by the integration of renewable energy sources has created an urgent need to improve energy efficiency in smart grids Conventional power grids with unidirectional power supply and midstream industries struggling to accommodate variable renewable energy and increased demand consumption. In response, this research explores the use of machine learning (ML) algorithms as a solution to increase energy efficiency, and presents a data-driven approach to address the challenges of modern smart grids meeting the solution for. The effectiveness of the algorithms is examined. Specifically, the research aims to (1) investigate how ML models can improve power delivery and reduce power consumption, (2) differ in key metrics such as accuracy and responsiveness, among others and regression, clustering, and neural networks -Time for performance testing ML algorithms, and (3) ML applications in smart networks Address practical challenges, such as data quality and computational requirements. To achieve these objectives, the study seeks to provide actionable insights for practitioners and researchers aiming to adopt ML solutions for sustainable energy use. The results of this study show that the ML algorithm significantly increases the energy consumption of smart grids. Through predictive modeling and optimization, the ML model achieved a 15% improvement in energy efficiency, a 25% reduction in peak demand, and an annual cost savings of approximately $200,000 Furthermore, predicting a ML-driven maintenance enabled early detection of potential grid issues, reducing downtime , technical losses and it has been reduced. These findings highlight the potential of ML to address today’s complex energy systems, delivering robust, scalable, and efficient solutions that support the integration and powering of renewable energy sources sustainable planning goals are advanced.
Copyright (c) 2024 Zinah Tareq Nayyef , Mayasa M. Abdulrahman, Neesrin ALi Kurdi https://creativecommons.org/licenses/by/4.0
The Effect of Strategic flexibility in achieving strategic success in the Jordanian Social Security Corporation
This research aims to assess how strategic success dimensions (specifically, adaptation and growth) are influenced by the independent variable of strategic agility, which is examined through its dimensions, namely capability and competitive agility. The study population consisted of all Jordanian Social Security Corporation managers and totaled (330) respondents. The study was based on a questionnaire to collect the required information. The study's questions were addressed, and the hypotheses were validated using statistical analysis, specifically employing SPSS. The study reached a set of results, the most important of which is the impact of strategic agility by its dimensions on the strategic success of Social Security Corporation-. The study recommended that the Social Security Corporation should pay attention to Strategic flexibility because of its significant impact in achieving strategic success and endeavor to develop modern electronic means of communication to facilitate the opening of dialogue among workers through these channels, which are considered a means to strengthen organizational capabilities and help the Social Security Corporation in increasing its capacity, In addition, the Social Security Corporation should adopt strategic thinking through its various programs
Copyright (c) 2024 Rateb AL Majali https://creativecommons.org/licenses/by/4.0