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.
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
Cybersecurity in Smart Grids: Protecting Critical Infrastructure from Cyber Attacks
The increasing digitalization of smart grids has brought numerous benefits in terms of efficiency, sustainability, and reliability, but it has also exposed these critical infrastructures to significant cybersecurity risks. This study investigates the cybersecurity challenges faced by smart grids, focusing on the vulnerabilities present in communication systems, control networks, and endpoint devices. The problem statement revolves around the growing complexity of smart grid infrastructures, which have become prime targets for cyberattacks such as denial-of-service (DoS), malware, and data breaches, threatening grid stability, data integrity, and national security. The primary objective of this study is to evaluate the current cybersecurity methods used in smart grids, such as encryption, firewalls, intrusion detection systems (IDS), and emerging technologies like artificial intelligence (AI) and blockchain. The study also aims to assess regulatory frameworks, such as the National Institute of Standards and Technology (NIST) and North American Electric Reliability Corporation Critical Infrastructure Protection (NERC CIP) standards and identify gaps in policy that need addressing. The results of the study demonstrate that AI-based anomaly detection systems significantly reduce attack detection times, averaging 2.5 seconds, with a low false positive rate of 7%. Blockchain technology, while increasing energy overhead by 15%, provides enhanced security for decentralized energy transactions. Multi-factor authentication (MFA) proved effective, blocking 98% of unauthorized access attempts. However, regulatory gaps were identified, especially in real-time monitoring and incident response. The study concludes that by adopting a layered security approach, integrating AI and blockchain technologies, and strengthening regulatory frameworks, smart grids can achieve a 75% improvement in cybersecurity efficacy. Continued efforts to address challenges such as legacy system integration, resource limitations, and evolving cyber threats are essential to securing the future of energy infrastructure.
Copyright (c) 2023 Dunya Zaman, Mahdi Mazinani https://creativecommons.org/licenses/by/4.0
Development of Smart Video Surveillance Systems: Technical and Security Challenges in Urban Environments
Video surveillance schemes have become a critical tool for safeguarding urban and industrial services, thanks to the rapid advancement in object recognition, tracking, and human activity analysis technologies. The achievement of these systems relies on background analysis, motion detection, and innovative solutions for real-time data processing and storage. These schemes have transitioned from analog setups to IP-based systems, offering more flexibility and remote control capabilities. However, they face important cybersecurity challenges, such as data breaches, bandwidth consumption, and high processing demands. This research explores the technological advancements in video surveillance systems, focusing on the security challenges and analytics in urban environments. Furthermore, it discourses strategies for optimizing system efficiency through modern compression algorithms and real-time human activity analysis.
Copyright (c) 2024 Suhaib Qassem Yahya Al-Hashemi, Majid Salal Naghmash , Ahmad Ghandour https://creativecommons.org/licenses/by/4.0
Improving IP Video Surveillance Systems: The Shift to Digital Networks and Security Challenges
In the numerical era, video surveillance schemes have fast evolved from analog technologies to IP-based systems, enabling widespread deployment in various environments such as streets, train stations, workplaces, factories, and homes. The incorporation of intelligent applications like facial recognition, object tracking, and event detection has expanded the utility of surveillance networks, allowing for the management of thousands of cameras across large geographic areas. This change has been facilitated by advancements in Internet Protocol (IP) technology, shifting surveillance from analog systems using coaxial cables to packet-switching systems in IPv6 and IPv4 networks. However, the extensive adoption of IP-based systems has introduced several challenges, including increased vulnerability to cyber attacks, bandwidth demands, processing power requirements, and network security issues. This research discovers the evolution of video surveillance systems, the technical and security challenges posed by IP-based networks, and potential solutions for improving their efficiency and security.
Copyright (c) 2024 Suhaib Qassem Yahya Al-Hashemi, Majid Salal Naghmash, Ahmad Ghandour https://creativecommons.org/licenses/by/4.0
Securing Cloud Computing Environments: An Analysis of Multi-Tenancy Vulnerabilities and Countermeasures
The rise of cloud computing has brought significant advancements in scalability, flexibility, and cost-efficiency, with multi-tenancy emerging as a core feature that allows multiple tenants to share the same physical infrastructure. However, this shared environment introduces serious security vulnerabilities, including data breaches, side-channel attacks, denial of service (DoS) attacks, and insecure APIs, all of which threaten the confidentiality, integrity, and availability of tenant data. The problem statement addressed in this study revolves around the challenge of securing multi-tenant environments, where resource sharing can expose tenants to risks stemming from improper isolation, malicious actors, and insufficient security measures. This study contributes to the field by providing a comprehensive analysis of these vulnerabilities and assessing the effectiveness of various countermeasures, such as enhanced isolation techniques, encryption, and access control mechanisms. A multi-faceted methodology, including literature review, case study analysis, risk assessment, and experimental simulations, was employed to evaluate these risks and their mitigation. The results reveal that, while security solutions like encryption and isolation can reduce the attack success rate to 5%, they do introduce a performance overhead of 12%. Additionally, the study highlights the importance of adhering to regulatory agreement frameworks such as GDPR and HIPAA, realizing a compliance score of 9.2. In conclusion, the study demonstrates that effective implementation of security measures can expressively mitigate risks in multi-tenant cloud settings. However, it also highlights the trade-offs between show and security, urging cloud providers and leaseholders to adopt a shared charge model for ongoing monitoring and protection. Future technologies, such as AI-driven security results and zero-trust styles, are also submitted as likely advances to further support the security of cloud calculating systems.
Copyright (c) 2024 Wahidah Hashim, Noor Al-Huda K. Hussein https://creativecommons.org/licenses/by/4.0
The Evolution of Cybersecurity Threats and Strategies for Effective Protection. A review
Cybersecurity has become an increasingly crucial priority for individuals, organizations, and governments due to the growing reliance on technology and internet connectivity. This paper explores the various threats posed by cyber-attacks, such as malware, ransomware, and phishing, that exploit security vulnerabilities to access sensitive information or disrupt systems. It emphasizes the need for comprehensive defense strategies to safeguard digital systems and ensure the continuity of vital processes. The paper also highlights the evolution of cyber-attacks, from simple viruses to sophisticated state-sponsored attacks, and outlines key elements for enhancing cybersecurity, including threat protection, incident response, and user awareness. The paper concludes by advocating for a proactive approach to cybersecurity through technological advancements, legal frameworks, and collaborative efforts at national and international levels.
Copyright (c) 2023 Hasan Ahmed Salman, Abdulazeez Alsajri https://creativecommons.org/licenses/by/4.0
Zero-Trust Architecture: Implementing and Evaluating Security Measures in Modern Enterprise Networks
Using the principles of quantum mechanics, quantum cryptography provides unprecedented security for communication. But as systems that contain sensitive information such as credit card numbers, email addresses, and names are evolving, it’s important to send or store this data to maintain security and privacy in places where security even in quantum to ensure that not only actual quantum communications, but also systems of these are protected There is also the challenge of protecting sensitive personal information that may be exchanged. In this paper, we tackle the challenge of properly anonymizing sensitive data in quantum cryptographic systems, in order to prevent its exposure, especially for scenarios where there is a risk of data harvesting or breach role. To overcome this problem, we used Presidio, a sophisticated tool for identifying sensitive and anonymous information, and were able to create a list of these data items by applying information to sample text with information a provides (Much) identification including name, credit card number, and email address (Anonymized_Name, Anonymized_Credit_Card, And Email Details). The results show that it is possible to efficiently anonymize private information without affecting communication integrity, adding additional security to quantum cryptographic systems. Our research concludes that Presidio provides a reliable means of protecting data privacy, reducing the likelihood of identity theft and data breaches. Although successful, this approach highlights the importance of sophisticated anonymization techniques in hybrid cryptography systems, and scaling issues but the findings of this study highlight how need to integrate anonymization technology with flexible communication management systems to improve security and compliance.
Copyright (c) 2023 Abeer Aljohani https://creativecommons.org/licenses/by/4.0
Human-Centric Cybersecurity: Understanding and Mitigating the Role of Human Error in Cyber Incidents
In this study, developments in cybersecurity continue to be treated as human deficiencies, such as the territory of policies, procedures and misconduct, mainly cultivating the floods of organizations. The important role of human factors in vulnerability To address this issue of negligence, the study presents a human-centered cybersecurity program that includes ongoing user training of the system, behavioral monitoring, and proposals including detection tools enhanced by AI. The research objectives are twofold: first, to examine how human actions contribute to cyber vulnerabilities, and second, to identify effective ways to mitigate these risks through user-centered approaches integration of roles found that the results show that human-centered policies significantly improve cybersecurity outcomes. Phishing error rates dropped from 15-20% to 5-10%, and password misuse dropped from 30-40% to 10-15%. The system reduced incident response time from 48-72 hours to even 24-36 hours, while increasing user engagement in safety actions. Additionally, the overall cost of a security breach is halved. This study emphasizes the need to address human behavior alongside technological processes to develop flexible and comprehensive cybersecurity policies.
Copyright (c) 2023 Swapnali N Tambe-Jagtap https://creativecommons.org/licenses/by/4.0
A Survey of Cryptographic Algorithms in Cybersecurity: From Classical Methods to Quantum-Resistant Solutions
As quantum computing technology evolves, it poses greater risks to current cryptography schemes such as RSA and Elliptic Curve Cryptography (ECC), widely used to secure digital communications These classical algorithms are based on mathematical problems that quantum algorithms such as Shore-Grover deal with. Both, can resolve much faster, making them vulnerable to quantum attack This has given rise to post-quantum cryptography (PQC), which focuses on developing quantum-resistant algorithms to protect data in the future of quantum computers classical cryptography Can Break This paper aims to provide a comprehensive insight into quantum-resistant cryptographic algorithms, including lattice-based, hash-based, code-based, and multivariable polynomial methods. It examines the efficiency, benefits, and challenges associated with implementing these algorithms in real-world applications. By comparing key parameters such as key size, encryption/decryption speed, and signature size, this study examines the relative strengths and limitations of post-quantum algorithms to their classical counterparts The results show that lattice-based algorithms , such as NTRU and Kyber, offer promising solutions with relative efficiency and manageable key sizes, making them potential candidates for quantum-resistant cryptography but other approaches , such as SPHINCS+ (hash-based) and McEliece (code-based), large -Face challenges of key size and slow encryption speed, which may limit their usefulness in some applications Despite these challenges, quantum post-cryptography is necessary to secure the future of digital communications.