is a peer-reviewed publication dedicated to cybersecurity, system design, and emerging technologies. It focuses on the challenges of the digital and quantum age, publishing research on quantum-safe cryptography, cyber threat detection, secure communication, privacy-preserving technologies, system vulnerability analysis, emerging technology security, incident response & forensics, and human factors in cybersecurity. The journal publishes two issues annually, promoting global collaboration and advancing research in these critical areas.
A Robust Intelligent Approach for Phishing URL Detection Using Hybrid Machine Learning
Phishing attacks are still the main and most severe vulnerability to cyber security through deceptive URLs for sensitive user information. Therefore, this paper proposes a framework based on deep learning techniques for phishing and legitimate URLs as an alternative to the currently employed blacklist-based detection approaches that have proven rather limited. The architecture includes one custom Convolutional Neural Network (CNN) and three Transfer Learning Architectures, ResNet50, InceptionV3, and VGG16 using representations of features based on URLs. All models under consideration shall be trained with Adam Optimizer and Binary Cross-Entropy loss function so that a fair comparison can be made under unified experimental set-up conditions. The set is broken down as 70% training, 10% validation, and 20% testing. Experimental results provide clear evidence that transfer learning models perform much better than the baseline CNN. The InceptionV3 model posted a validation accuracy of 100% leading the pack, followed by VGG16 at 98.96%. ResNet50 could muster only 97.92%. The proposed CNN model has also achieved quite competitive performance with a validation accuracy above 97%. Therefore, these results are explicit in confirming the effectiveness, robustness, and generalization capability of deep learning architectures toward the problem of phishing URL detection. This proposed framework will go a long way in ensuring web security is beefed up as it creates a barrier against all evolving cyber threats.
Copyright (c) 2025 Hind Abdulkareem Abdalrazaq (Author) https://creativecommons.org/licenses/by/4.0/
Quantum-Resilient System Architectures: Designing Secure Infrastructure for Next-Generation Computing
There's a lot of hope that, with quantum computing, the future can look much brighter and be focused on solving a lot of complex issues we face in modern society. The power to give and manipulate information with quantum systems can be game-changing for fields such as secure data transfer, unmanageable optimization, drug discovery in medical applications, and certain facets of artificial intelligence. Algorithms over the discouraging search spaces would be able to break existing encryption, and significant progress has been achieved in algorithmic attacks on both secret key (cryptographic hashes and message authentication codes) and public key systems. A thorough survey is required so that the state-of-the-art quantum resilient theoretical and experimental methods and models may be understood from academia as well as from industry, and new directions which need to be promoted are suggested. It is important to investigate the possible security threats, challenges, impact and risk factors introduced by the integration of quantum computing in application systems, middleware, quantum system core components networks and cloud services critical infrastructures standards and regulation.
Copyright (c) 2025 Uqba bn Nafaa Mohammed (Author) https://creativecommons.org/licenses/by/4.0/
Mobile App for Purchasing Tickets: Setram Tickets Ouargla, Algeria
This project presents the design and implementation of "Setram Go", a hybrid mobile application forpurchasing public transport tickets and managing subscriptions in Ouargla, Algeria. During theconception phase, several UML diagrams were used — including use case diagrams, class diagrams,and activity diagrams — to model the system architecture, user interactions, and core functionalities.The application aims to solve problems related to manual ticketing, the absence of electronicpayments, and limited accessibility. Developed using React Native (frontend), Express.js (backend),and MongoDB, the app supports electronic wallet recharge, QR code validation, bilingual interface(Arabic/English), and real-time station mapping. Functional testing confirmed the application's corecapabilities, with future work planned to integrate real payment gateways, offline functionality, and improved security.
Copyright (c) 2025 AZZAOUI Nadjet, Bessedike Zakaria, Touhami Mohammed Lazhar, Osmani Youcef, Koudia Souhil (Author) https://creativecommons.org/licenses/by/4.0/
Comparison between HTTP and HTTPS based on Software engineering Concepts
Hypertext Transfer Protocol Secure (https) has many advantages in terms of performance and security; most importantly, it achieves higher quality standards than Hypertext Transfer Protocol (http). All browsers strongly advise users to trust websites that use HTTPS because it is the only way to protect themselves from a number of risks, attacks, in this paper, the most important characteristics of HTTPS versus HTTP will discussed, and Tested based on software engineering concepts and selected test tool in comparison with Software Quality Factors.
Copyright (c) 2025 Ahmed Saleem Abbas , Noor Thamer Mahmood, Rusul Khalil Hussein (Author) https://creativecommons.org/licenses/by/4.0/
Developing A Lightweight Homomorphic Encryption Technique for Secure Data Transmission
This paper proposes a lightweight homomorphic encryption (HE) technique designed for secure data transmission in resource-constrained environments such as IoT, sensor networks, and edge computing platforms. The method integrates a modified AES structure—in which the number of rounds and key-expansion operations are optimized—with RSA’s multiplicative homomorphism to support limited encrypted computation while minimizing computational overhead. The modified AES reduces complexity in the Mix Columns and Key Expansion stages without compromising cryptographic soundness. Experiments conducted on a 32-bit ARM-based emulator (100 MHz) and Arduino-class microcontroller demonstrate reduced encryption time, lower energy consumption, and improved throughput compared with standard AES, RSA, and AES+RSA hybrids. Preliminary comparisons with lightweight HE baselines (LWE-based and CKKS variants) indicate promising efficiency advantages for constrained hardware. Security analysis confirms that modifications do not weaken resistance against known cryptanalytic attacks. The resulting framework is suitable for healthcare monitoring, smart grids, industrial IoT, and privacy-preserving cloud analytics.
Copyright (c) 2025 Sumaira Bashir, Amit Sharma (Author) https://creativecommons.org/licenses/by/4.0/
Quantum Machine Learning for Cyber Threat Detection: Enhancing Security in the Quantum Era
The cat-and-mouse game is endless. On the one hand, the security industry is ceaselessly countering security technologies to immunize against perceptible threats. On the other hand, adversaries are always attempting to devise cutting-edge exfiltration schemes for potential return on investment. Ever since the Federal Bureau of Investigation (FBI) released the Flash report that hinges on raw indicators to unveil LICAT backdoor on Joomla websites running PHP, adversaries tweaked their infection vectors to avoid detectable host-based artifacts. Economic and geopolitical aspects also spurred the entrenchment of Advanced Persistent Threats (APTs). Major players in cyber industry are investing manifold resources into protracted espionage campaigns by concentrating on high-value targets. While research in QC is progressing, there are also challenges in harnessing the technology’s full capabilities. Much like conventional computing requiring some understanding about a computer’s architecture, a fundamental understanding of quantum mechanics is needed to comprehend QC . To explore how advances in quantum may be applied in cybersecurity tasks, developments in this technology are reviewed. Quantum computing can help to deal with cybersecurity tasks by enhancing problem complexity due to its advantages in performing complex tasks effectively. Cybersecurity tasks may be substantially complex, requiring understanding non-local correlations and collecting a large quantity of data points in different fidelities and complexities.
Copyright (c) 2025 Mohammed abdulhamza Noor (Author) https://creativecommons.org/licenses/by/4.0/
Securing IoT Devices against Quantum Threats:Developing a Framework for Future-Proof Encryption
Quantum Computers are designed to be sufficiently large to be able to efficiently solve specific problems that would take an impractical amount of time to solve using classical computers. The most widely discussed application of quantum computing technology is more powerful algorithms, which efficiently solves all instances of integer factorization—a number theoretic problem that is the foundation of the most of the existing public-key encryption schemes. This means that all public-key cryptographic systems commonly used on the Internet for security will become as insecure as an open communication channel, easy to eavesdrop on or to tamper with. Moreover, traffic passes through numerous fixed, mobile and personal sensors connected to the Internet. This extremely large amount of data becomes the “air” of IoT and only a small part of it will be automatically analysed for a specific purpose. Nevertheless, an important part of the immense amount of data exchanged across the network is sensitive. Presently, these data communication and storage are protected by encryption, but that means of protection will be lost to a future quantum adversary. Because of these urgent necessities, products implementing quantum-resistant security solutions can be built and put into operation in a “post-quantum world”. This must be done now because cryptography research is currently inventing and standardizing new cryptographic algorithms. Once the new algorithms are widely and publicly agreed upon, systems with old encryption will remain vulnerable. This topic is motivated by the above.
Copyright (c) 2025 Noor Thamer Mahmood (Author) https://creativecommons.org/licenses/by/4.0/
Post-Quantum Digital Forensics: Investigating Cybercrime in a Quantum-Compromised Landscape
Although quantum computing poses a number of opportunities, it also represents an emergent threat to conventional cryptographic practices, particularly to digital forensics in cybercrime prosecution. While post-quantum cryptanalysis is still in its infancy, early stages of quantum supremacy are being achieved. As feature sets of quantum computing develop, substantial emerging technologies are expected to be available in the next five years, including Shor’s and Grover’s algorithms. While the former can be used for the fast factoring of large integers, this implies that public-key cryptographic systems will be efficiently calculable, facilitating the detection of digital communication channels. Simultaneously, this allows encrypted data storage to be broken, undermining claims protection. Meta data, for example, phone numbers dialed, contact lists or text length, is vulnerable through Grover’s algorithm, which provides a quadratic acceleration of brute force attacks.
Copyright (c) 2025 Sarmad Jawad, Huda Kadhum Ayoob, Shahad Fahim Aljanabi, Ali Salah Mahdi Alobaidy (Author) https://creativecommons.org/licenses/by/4.0/
Human-Centered Cybersecurity: Examining Ethical and Societal Considerations of Quantum Technologies
In the past decade, quantum technologies have made remarkable progress. Quantum technologies are able to collect and utilize quantum phenomena to facilitate the accomplishment of tasks deemed infeasible by conventional means. Developments of quantum computers and cryptographic protocols have drawn extensive attention of both academia and industry. On one hand, quantum computation facilitates the rapid progress of various tasks/computing. The prime examples are the algorithms for simulating quantum systems, quantum cryptography, and solving the integer factorization and discrete logarithm problems through an algorithm. These advances have important and far-reaching discoveries: the first physical quantum computer was introduced in 2011; quantum cryptography has been used in widely-deployed devices; the first quantum-resistant algorithms were standardized in 2017. Vendors have recently launched their cloud-accessible quantum computers. This is expected to stimulate the rapid development of the quantum industry and its derivative technologies. However, the advantage in the rise of quantum technologies comes concomitant with various new challenges as well. Intractable problems overcome by the conventional methods become vulnerable to their quantum counterparts. New vulnerabilities have been spotted prior to the advent of quantum computers – more sophisticated attacks are underway, and organizations may indeed be breached already but remain unaware of. Furthermore, almost all advanced security technologies rely on the tacit assumption that the computational task required by the adversary is computationally harder than the security-related one. Therefore, the backgrounds of cyber security and quantum technologies call for a thorough rethinking of their avoidances. Under such circumstance, the scientific community should openly discuss the ethical and societal considerations of quantum technologies – what responsible researchers and developers should do to protect the world from their prospective vulnerabilities and harms. This comprehensive survey intends to explore a discussion of these emerging issues.
Copyright (c) 2025 Wafaa Sallal Abbood, Ali Kahtan Lelo, Nisreen Saad Hadi, Mohammed Abdulhamza Noor (Author) https://creativecommons.org/licenses/by/4.0/
A Comparative Study of Post-Quantum Cryptographic Algorithms Assessing Security and Performance Trade-offs
Lattice-based cryptography is encryption that uses lattice issues as the basis of a conjecture of the complexity of the worst-case problem. This complexity is considered hard in the world of classical and quantum providing for a primitives design of lattice-based cryptography algorithms. The NIST held a contest to find the best algorithm to implement. This paper compares three finalists of that contest by analyzing the theoretical basis of the lattice problems they hold, with the security of the worst case of the attack through some algorithms, lattices with underlying problems, construction of cryptographic algorithms, and comparative analysis of three finalists of the lattice schemes of the NIST competition. Comparison includes the safety and robustness of the lattice problem used the cryptographic algorithm that is built and finally there will be an analysis in a computational time context to discuss the efficiency algorithm created. Two advantages and disadvantages of each the algorithm finalist are implemented and also described.
Copyright (c) 2025 Mohammed Hasan Al-Dulaimi, Suha Kamal Al-Dulaimi (Author) https://creativecommons.org/licenses/by/4.0/
A Hybrid Deep Learning Approach for Arabic Speech Emotion Recognition Using Acoustic and Spectrogram Features
Arabic Speech Emotion Recognition (ASER) presents unique challenges due to linguistic diversity, phonetic complexity, and the limited availability of labeled datasets. This work presents a hybrid deep learning model that integrates a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN) to effectively classify emotions from Arabic speech data, using the KEDAS dataset. The model is designed to process two complementary sets of features: acoustic features, such as low-level descriptors, as well as Mel-spectrogram features that capture time-frequency information. Experimental results demonstrate that the hybrid architecture effectively leverages the strengths of both feature types, achieving a high accuracy of 98% in emotion recognition.
Copyright (c) 2025 Dalal Djeridi, Bachir Said, Mourad Belhadj (Author) https://creativecommons.org/licenses/by/4.0/
Enhancing Lithology Prediction through Preprocessing Techniques and Machine Learning Models
Lithology classification through well log interpretation is a fundamental task in reservoir characterization, enabling accurate delineation of subsurface formations and assessment of hydrocarbon potential. However, measurements are rarely full, and missing data intervals are prevalent due to operational difficulties or logging device failure. Thus, imputation of missing data from down-hole well logs is a prevalent issue in subsurface processes. Our work a strong emphasis on the preprocessing phase and data imputation, acknowledging that missing data in well logging is a prevalent problem that can have a major impact on classification results. Our work is part of the FORCE2020 Lithology Classification Competition. Our method underlines how important extensive data preprocessing is for improving model performance, including regression-based imputation, normalization, and class balancing by SMOTE. Traditional models like Random Forest and XGBoost were able to produce reliable results in the challenging FORCE2020 Lithology Classification. By leveraging multiple models, we aim to enhance the accuracy and robustness of our predictions, addressing the challenges posed by missing data and ensuring a more reliable classification process. We show that the Random Forest model obtains the greatest accuracy of 95% using the FORCE 2020 dataset from 118 wells in the Norwegian Sea. This study emphasizes how crucial thorough data imputation and preprocessing techniques are to raising the precision and dependability of lithology classification.
Copyright (c) 2025 Cherif Mesroua, Ibrahim Lahouel, Basma Hamrouni, Khadra Bouanane, Faiza Zidouni, Wafa Kafi (Author) https://creativecommons.org/licenses/by/4.0/
Enhancement of GPS Signals Acquisition in non-Homogeneous Environment using the OS-CFAR Techniques with Optimization Technique
In this paper, we propose to use metaheuristic optimization techniques to improve the adaptive acquisition of the global navigation satellite system (GNSS) in both homogeneous and non-homogeneous environments, The main objective of this work is to optimize the thresholding of the Constant False Alarm Rate (OS-CFAR) in Rayleigh fading channels we compare the result with the base detector CA-CFAR . In GNSS acquisition, the pilot and data blocks may have different thresholds. Therefore, the optimization will focus on two scaling factors (T and k). Two fusion rules have been used here "AND" and "OR". Due to their performance in different optimization problems, metaheuristics have been chosen as the tool to solve this type of problem. The simulation results show that the optimized thresholds have a significant impact on the performance of the acquisition system.
Copyright (c) 2025 El bahdja OURFELLA, Sabra BENKRINAH (Author) https://creativecommons.org/licenses/by/4.0/
Transforming Algerian Industry Through Intelligence: Academic Insights on AI And Digital Transition
This article explores the transformative potential of Artificial Intelligence (AI) in reshaping Algeria’s industrial landscape within the broader framework of digital transition. Focusing on key sectors such as energy, agriculture, and manufacturing, the study examines how AI technologies ranging from machine learning and computer vision to natural language processing are being leveraged to enhance efficiency, safety, and decision-making across industrial operations. Through a detailed case study of the national energy leader, Sonatrach, the research highlights measurable improvements including reduced equipment downtime, more accurate resource forecasting, and accelerated data analysis. The article also discusses the alignment of these technological advancements with Algeria’s national digital strategy, while critically addressing persistent challenges such as data infrastructure, workforce readiness, and sectoral scalability. Ultimately, this study offers academic and policy-driven insights into how AI can serve as a catalyst for sustainable industrial modernization in Algeria .
Copyright (c) 2025 Abdelkrim BENALI, Somia BENALI (Author) https://creativecommons.org/licenses/by/4.0/
Quantum Cryptanalysis: Evaluating the Impact of Shor’s and Grover’s Algorithms on Modern Encryption Standards
Motivated by Shor’s algorithm and its attractive features, the milestone results in quantum cryptanalysis were the discovery of quantum differential and linear cryptanalysis. In quantum cryptanalysis, quantum distinguishers imitate the classical setting. Details differ; however, as the distinguisher in the quantum setting can take maximum coset superposition of all possible subsets of plaintext pairs. Using quantum measurements, the winning gap of the game is exploited so that a distinguisher can still guess the secret key perfectly with a constant probability. Symmetric primitives also suffer reduced ideal security in the quantum world, but this security reduction is less drastic than for most asymmetric primitives. Stream ciphers, block ciphers, and cryptographic hash functions are primitives that are believed to resist quantum attacks. Analogs to Shor’s algorithm may say how hard it is to build a quantum computer, but devising quantum algorithms to outperform the best classical attacks for widely-used cryptographic primitives is probably outside the reach of current even if exponentially growing noisy intermediate-scale quantum (NISQ) computers.
Copyright (c) 2025 Mithal Hadi Jebur, Sumar Mohamed Khaleel (Author) https://creativecommons.org/licenses/by/4.0/
The Role of Deep Learning in Enhancing Solar Panel Efficiency: A Review of Models and Metrics
The rising desire to get long-term power has stepped up the utilisation of solar photovoltaic (PV) systems all over the world. Nonetheless, keeping the solar panels in optimal performance is a major concern because of the changeable environmental conditions, wear and tear of the equipment and breakdown of operations. Deep learning (DL) has become an efficient solution of offering sophistication in predicting solar power generation, fault detection, and optimization of the performance. The review will go in depth over several DL methodologies, such as, CNN, LSTM, Transformer-based models, Reinforcement Learning, GANs, and hybrid architectures of CNN-LSTM. The implementation of each model, performance metrics of MAE, RMSE, R2, and MAPE as well as weak points and advantages of each model, are provided in their relation to increasing PV efficiency. Moreover, the paper covers real-life data, real-life implementations, and comparative performance of these models in addressing critical issues in the solar energy systems. Nonetheless, despite the significant progress, there are still a number of challenges that need to be addressed, among which are the data sparsity, inability to generalize the models to different areas of interest, computational limitations, and inability to interpret the models. To resolve these problems, future directions outlined by the present paper include the creation of lightweight models of edge-AI, the use of transfer learning to achieve regional adaptability, the possibility of Explainable AI (XAI), and the combination of deep learning and IoT and digital twin technology. I would say that the given comprehensive review is an excellent source of information that can be used by researchers, engineers, and policymakers who want to use deep learning to make solar panels as efficient as possible to deliver more resilient, reliable, and sustainable energy delivery.
Copyright (c) 2025 Amjed Abbas Ahmed, Haneen Siraj Ibrahim, Ihtiram Raza Khan, Rabiu Aliyu Abdulkadir, Abubakar Amoo Akinlawon, Munzali Alhassan (Author) https://creativecommons.org/licenses/by/4.0/
Exploring the Techniques and Challenges of Privacy-Preserving Data Sharing in Quantum-Enabled Networks
Privacy-preserving data-sharing mechanisms traditionally rely on data anonymization techniques, which aim to dissociate individual records from their corresponding data subjects in order to transform sensitive information into a form that minimizes the risk of re-identification. In parallel, trust-broker models have emerged as an additional layer of protection, whereby authorized data providers disclose controlled contact information that enables potential data users to request access under predefined conditions determined by the data owner. In contemporary data-sharing environments, cryptography remains a fundamental pillar for ensuring confidentiality and integrity during data exchange. Cryptographic protection typically involves two operations: an encryption process, in which the sender transforms plaintext into ciphertext that is unintelligible to unauthorized entities, and a decryption process, in which only the intended recipient restores the ciphertext to its original form. Both operations rely on a cryptographic algorithm paired with one or more secret keys. Symmetric-key cryptography employs a single shared key for both encryption and decryption. In contrast, asymmetric cryptography uses a mathematically linked pair of keys, where one key is used for encryption and the other for decryption. When the encryption key is publicly available while the corresponding decryption key remains private, only the legitimate recipient holding the private key is capable of recovering the plaintext. This cryptographic structure forms the basis for securing data confidentiality against unauthorized access in both classical and quantum-aware communication systems.
Copyright (c) 2025 Zaid Ibrahim Rasool, Nisreen Saad Hadi (Author) https://creativecommons.org/licenses/by/4.0/
Artificial Intelligence in the Era of Educational Big data: A Systematic Review
In this paper, a systematic review of the latest research on the deployment of AI in educational Big data analytics is provided including its applications, challenges and future research possibilities. The databases IEEE Xplore, ScienceDirect, SpringerLink, Scopus, and Web of Science were systematically searched to find peer-reviewed journal articles written by Published by Elsevier B.V.8676,during 2020–2025. Fifty studies are included in the qualitative synthesis according to pre-designed inclusion/exclusion criteria. The results highlight four key themes for the application of AI: predictive analytics for predicting academic performance, personalised and adaptive learning systems, learning analytics for supporting teacher and institution decision making and institutional decision support using educational data. Challenges, including technical limitations, institutional readiness, and ethical considerations in privacy, bias, and transparency are also discussed.In addition, this analysis points to gaps in literature and paves ways for researchers to extend the current frontiers of research, including the design of interpretable AI models, multi-language tools, and privacy-preserving approaches. The findings offer important implications for researchers, educators, and policymakers striving to harness AI to design better and fairer educational systems.
Copyright (c) 2025 Ali Hussein Khalaf AL-Sammarraie (Author) https://creativecommons.org/licenses/by/4.0/
Leveraging deep Learning for Efficient Intrusion Detection in IoT Networks
Internet of Things (IoT) technology is experiencing rapid development and increasing use in a variety of applications, making it a potential target for cyber-attacks. Machine learning and deep neural network techniques are an effective way to address these challenges and improve IoT security. This research aims to design a deep learning techniques for intrusion detection in an Internet of Things environment with limited resources. The research focuses on improving the efficiency and effectiveness of current model using artificial intelligence and LSTM algorithms, ensuring reliable and effective security in the IoT environment. The proposed model is evaluated using a realistic data set, Canadian Institute for Cybersecurity Internet of Things 2023 Dataset (CICIoT2023) devices, and using performance metrics, namely Accuracy, Precision, F1 Score, and Recall. The results show its compatibility and effectiveness in a real environment, with 99.1% accuracy recorded. This paper is considered an important contribution to the field of IoT security and provides an effective methodology for developing advanced security solutions in the IoT environment that enhance traffic analysis, identify abnormal behavior, and take the necessary measures.
Copyright (c) 2025 Noor Thamer, Suhad Hatem Jihad, Sumar Mohamed Khaleel, Ahmed Saleem Abbas (Author) https://creativecommons.org/licenses/by/4.0/
Emerging Trends and AI-Driven Defense Mechanisms in Cybersecurity: A Novel Framework for Threat Prediction and Prevention
Rapid digital ecosystem growth has made cybersecurity a major issue nowadays. As gadgets, cloud platforms, and critical infrastructures become more interconnected, fraudsters may exploit weaknesses with unparalleled sophistication. Advanced threats including ransomware, deepfake-driven phishing, supply-chain breaches, and AI-powered assaults are beyond firewalls and intrusion detection systems. This paper presents a hybrid cybersecurity system that uses AI, blockchain, and Zero Trust to anticipate, prevent, and mitigate intrusions in real time. Our system uses machine learning to identify anomalies and decentralized, blockchain-based trust management to safeguard data and authentication. A proactive strategy improves detection accuracy, decreases false positives, and builds resistance to emerging threats. Trials utilizing benchmark intrusion detection datasets show that the framework outperforms standard systems. Its use in high-risk industries including banking, healthcare, and industrial IoT is shown by the results. For a safer digital future, our study develops adaptable, intelligent, and scalable cyber protection methods.
Copyright (c) 2025 Muna A. Radhi, Majd S. Ahmed, Ethar Abdul Wahhab Hachim, Zeyad Farooq Lutfi (Author) https://creativecommons.org/licenses/by/4.0/
Mitigating Cybersecurity Threats in Autonomous Vehicles: A Real-World Case Study on Protecting V2X Communication Against Remote Hacking Attempts
As of now, maintaining the security and dependability of V2X communication remains an open issue due to the complexity and vulnerability of wireless networking technologies for Autonomous Vehicles (AVs). This study is conducted to address the research gap. The initial research activities are to routinely test the security vulnerability of the communication technologies in AVs, which encompass vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and vehicle-to-device (V2D) communication. Industry tools and software licenses are used to perform these experimental tests. The results reveal that malicious cyber-attacks targeting V2X communication are successful. Building on the results, a model-based SDLC and a set of guidelines are proposed to securely develop software for AVs 2. Participants of the project are also trained in these newly developed methodologies to enhance the development security of the AVs in the future.
Copyright (c) 2024 Yasmin Makki Mohialden, Nadia Mahmood Hussien, Samira Abdul Kader Hussain (Author) https://creativecommons.org/licenses/by/4.0/
AI-Driven Optimization of Cloud Resource Allocation for Personalized Medical Imaging in Hospitals: A Case Study from a Major Medical Center
This study is to introduce a cloud-based, AI-driven architecture that can address the above-mentioned challenges properly and demonstrate its feasibility through real-scale implementations and practical testing in a major traditional hospital in Asia that typically operates a variety of diagnostic imaging services. A typical scenario is that vital organ medical images need immediate attention and response from any physician and hospital. CT brain imaging and PET-CT, Gallium imaging of the whole body, glucose, and myocardial ischemia will be implemented and tested in this project. These diagnostic imaging devices usually generate cross-sectional images of patients and typically produce an average of 500 - 600 high-resolution images each. In the local hospital, the human eye ballot reading and deep learning mean image classifications take a considerable amount of time to present the results. This project will take advantage of the cloud, AI, and web-based technology to rapidly assist physicians in providing better medical services. The research objectives of this project are summarized as follows: (1) Gaining experience and lessons by deploying AI technology on a real hospital scale. AI/HA medical imaging services operated in the cloud can be made available for other hospitals to use.
Copyright (c) 2024 Mohammed Mukhtar Magaji, Usman Aliyu Magaji (Author) https://creativecommons.org/licenses/by/4.0/
Implementing Real-Time Edge AI for Anomaly Detection in Smart Grids: A Pilot Study on Power Distribution Networks
In this pilot study, we aim to establish the changes needed to investigate whether edge AI can lead to real-time anomaly detection in the grid. In designing anomaly detection techniques, numerous other AI techniques such as artificial neural networks, among many others, have been investigated in previous works. However, only a few investigate their use for real-time anomaly detection in power systems. This pilot research looks into the possibility of developing a real-time anomaly detection methodology for smart grids. A part of this is casting the anomaly detection algorithm in a way it can be deployed on the edge. This section aims to review a literature survey that contains all the methods and algorithms used in the anomaly detection process. The section starts by addressing the need for the intervention of anomaly detection systems to mitigate the risk of attacks. Then the survey presents the well-established methods of anomaly detection. The last part of this section will review the previous attempt of moving anomaly detection to the edge of networks. In-depth details of each algorithm will be presented in the next section.
Copyright (c) 2024 Rabiu Aliyu Abdulkadi, Abdullahi Garba Musa (Author) https://creativecommons.org/licenses/by/4.0/
The Role of Cybersecurity in Digital Film Production and Distribution: Challenges and Solutions
Digital film production and how they are distributed have altered production and distribution for efficiencies, creativity and access to the world. But it has come with a series of cybersecurity problems, such as data breaches which exposes sensitive personal or corporate data, which leads to identity theft, reputation damage and financial loss. Intellectual property theft is another problem being faced by the film industry in the production process to digital distribution network vulnerabilities. The focus of this article is cybersecurity as an integral component of digital movie properties, including piracy, ransomware and insider threats. Using actual case studies and finding out what’s happening in the digital film ecosystem we can also make the case for strong security architectures. The paper suggests practical solutions such as advanced encryption technology, DRMs and new technologies such as blockchain and AI-based cybersecurity solutions. Overall, this research shows how we need a holistic cybersecurity approach to protect the future of digital film production and distribution.
Copyright (c) 2024 Mehtab Alam, Shahi Raza Khan, Ihtiram Raza Khan, Abdul Qadir Siddiquee (Author) https://creativecommons.org/licenses/by/4.0/
Random Forest and LSTM Hybrid Model for Detecting DDoS Attacks in Healthcare IoT Networks
The growing integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care and operational efficiency. However, this advancement comes with vast cybersecurity demanding situations, as IoT devices are exceedingly susceptible to diverse cyber-attacks, which include statistics breaches, denial-of-provider (DoS) attacks, and unauthorized get right of entry to. This look at proposes a robust cyber-assault detection machine through leveraging Random Forest (RF) and Long Short-Term Memory (LSTM) algorithms, which integrate static sample popularity with sequential facts analysis. RF is utilized for its performance in coping with dependent statistics, along with network visitors and tool logs, at the same time as LSTM excels in analyzing time-collection facts, allowing the detection of evolving threats. The proposed hybrid RF-LSTM version became evaluated using real-global IoT healthcare datasets. RF established high accuracy in detecting static anomalies, accomplishing an accuracy of ninety four% and a precision of ninety three%. LSTM excelled in coping with temporal dependencies, reaching an F1 score of 91% and minimizing fake negatives. The integration of both algorithms more desirable the gadget's capability to stumble on a huge variety of attacks, reaching an common detection accuracy of ninety seven% in real-time scenarios. This research highlights the capability of hybrid fashions in ensuring IoT safety and mitigating cyber threats in healthcare environments, making sure patient protection and information integrity.
Copyright (c) 2024 Atheer Alaa Hammad (Author) https://creativecommons.org/licenses/by/4.0/
Exploring Quantum Computing: Potential Applications and Current Challenges in Algorithm Design
Quantum supremacy refers to the experimental demonstration that a quantum computing device performs a calculation that no classical computer can match in a reasonable time. Such machines would not be useful for solving problems of practical interest, but they would provide a proof of principle that larger and more useful machines could be built. The gap between classical and quantum computing is potentially enormous, and the apparent ease with which quantum states can be prepared, manipulated, and measured has led many to believe that quantum devices might one day outperform classical hardware at solving a broad range of computational tasks. The most immediate goal of progress in quantum engineering is the construction of a device that passes the proofs of principle of quantum supremacy experiments. They would transform a state that can be initialized neutrally, evolve it under a controlled Hamiltonian, then sample from the output distribution. Such devices must possess sufficient connectivity and low noise but can be cared for by classical software. Their demonstration would mark a historic milestone in the development of quantum technology, providing intrigue and awe but little societal benefit. However, if they can be produced with sufficient fidelity and scale, there is hope that more generally applicable quantum engines might be constructed that could exploit the exponential speedup to outperform classical computers for problems of deep societal importance.
Copyright (c) 2024 Amjed A. Ahmed, Mohammad K. Hasan (Author) https://creativecommons.org/licenses/by-nc/4.0
The Role of Blockchain Technology in Enhancing Data Integrity and Transparency Across Industries
This technology applies cryptographic and consensus mechanisms that secure communications between participating peers. Each transaction on the blockchain is assigned a timestamp, thus establishing the transaction's historical order and preventing it from being altered. Newer transactions are added to the blockchain only after several participating nodes have approved them. Transactions approved on the same block form a distributed consensus around this block. If such a transaction is modified, the transaction's timestamp would be different from that recorded on the block containing it, and the distributed consensus would no longer hold. The blockchain would increase its size with the addition of subsequent blocks appended to it, making it computationally expensive to modify older transactions. These concerns are further fueled by the potential risk of data leaks or hacks that could occur directly or indirectly through third-party system providers. Many organizations do not fully comprehend how third parties could potentially gain access to sensitive information despite using various cybersecurity methods such as data encryption. Some online platforms contain shared databases that may store data collected by large networks of businesses, which allows them to gain a more thorough profile of an individual. This profiling is alarming consumers and resulting in distrust in how their data is used. There is a growing movement to decide whether personal data should be regarded as personal property instead of a product owned by online platforms. Consumer unrest has resulted in actions such as the General Data Protection Regulation (GDPR), which attempts to diminish online activity surveillance. Blockchain data is typically stored using linked data structures implemented in computer program language using Applications Programming Interfaces (APIs). A frequently used data structure is a Merkle tree, where a binary tree is used to store the information, creating a binary search tree that allows verifying the information with only a small subset of the data.
Copyright (c) 2024 Yasmin Makki Mohialden, Nadia Mahmood Hussien (Author) https://creativecommons.org/licenses/by/4.0/
Deep Learning for Computer Vision: Innovations in Image Recognition and Processing Techniques
Deep learning is a key area of research in the field of computer vision, image processing and bioinformatics. The techniques of deep learning generally are divided into three categories namely Convolutional Neural Networks (CNN), Restricted Boltzmann Machines (RBM), Stacked RBM and HOG (Histograms of oriented Gradient) feature extraction, Convolutional Neural Networks as a Database (CNN as D). Additionally, one in few deep learning architectures which is gaining popularity and is frequently used in the field of computer vision and image processing is Extreme Learning Machine and ensemble of Extreme Learning Machine and CNN. It attempts to survey the recent advances in deep learning researchers and the application of these algorithm in the field of computer vision. Mainly focusing on the deep learning methods and algorithms rather than image processing and computer vision methods, this work inspects deep learning techniques which are widely and commonly used in the field of computer vision image detection and processing like CNN, DBN, RBM and HMM as well as various applications of these techniques. Applications of deep learning techniques in computer vision are image classification, object recognition and detection. Along with the recent works and the future scope for deep learning methods in the field of computer vision and image processing is presented.
Copyright (c) 2024 Akeel Sh. Mahmoud, Sahar Hamad Ahmed (Author) https://creativecommons.org/licenses/by/4.0/
Advancements in Cybersecurity: Novel Approaches to Protecting Against Emerging Threats and Vulnerabilities
Cyber threats have expanded from theft of information to the misuse of turned data for intent to manipulate business exposures or to harm and disrupt healthcare operations. Globally, the medical cybersecurity market is $12.6 billion. It is required that medical device manufacturers integrate cybersecurity during the design phase of the product life cycle. Cybersecurity should not have been an afterthought. It is a continuous process that works best if all involved listen to one another. Solutions, technology, and methodologies exist but are not utilized across the healthcare industry to build Cybersecurity by Design medical devices. A risked-based approach is proposed where medical devices are designed, tested, manufactured, and operated with strong consideration of security. The operators and staff should be instructed about the dangers of not operating the device properly. The network surrounding the device should be safeguarded as one part of a wider cybersecurity chain.
Copyright (c) 2024 CyberSystem Journal https://creativecommons.org/licenses/by-nc/4.0
Edge Computing Vs. Cloud Computing: Evaluating Performance, Scalability, and Security in Modern Applications
Emerging computing paradigms such as edge computing and cloudlet computing offer distributed computing resources in proximity to the sources and end users. By moving the processing closer to the data source and consumer, these novel paradigms improve the performance by decreasing latencies, and thus by enabling a faster and real-time provision of services. In addition, they can be used to offload massive amounts of raw data for processing on more powerful computing resources instead of sending all the data through the network towards the clouds, thus saving network bandwidth and mitigating congestion. These computing paradigms also enable more flexible service provision, as the resources can be easily scaled based on the demand in a productive and cost-effective way compared to the traditional cloud computing infrastructures. The rapid evolution of modern technologies has led to the emergence of a myriad of applications in different domains such as Industry 4.0, the Internet of Things, smart cities, health-care, and transportation. The typical processing approach in these applications involves data travel from sources to machines or servers for processing and then back to the end users. Since the data travel distance might be long depending on network and topological structures, traditional cloud computing is found to be insufficient to fulfil the performance, scalability, and security requirements of many modern applications. Therefore, there is an urgent need for novel computing paradigms which close the processing gap between data sources and consumers.