Detection of malicious UAVs based on secure authentication over the Internet of Drones (IoD) Albandari Alsumayt, Majid Alshammari, Fatemah H. Alghamedy, Zeyad AlFawaer, Nahla El-Haggar, Anwar D. Alhejaili, Sameer Alghanmi, Faisal Alamri, May Issa Aldossary, Alanod F. Almiman, Amal A. Alshaer Multimedia Tools and Applications, 2026 The Internet of Drones (IoD) is experiencing significant growth across military, commercial, and civilian applications due to its unique attributes, including high mobility and three-dimensional movement. However, the reliance on unencrypted wireless communication and the limited computational capabilities of drones makes them vulnerable to various cyber-attacks. These vulnerabilities expose IoD networks to threats such as man-in-the-middle attacks, impersonation, credential leakage, GPS spoofing, and drone hijacking. To address these challenges, the development of a robust and highly secure protocol is essential. This paper presents an innovative approach for detecting malicious drones utilizing the Conversation to Handshake Authentication (CTHA) method. Our proposed solution employs sequential procedures and deep learning techniques to distinguish between legitimate and malicious drones effectively. Furthermore, we incorporate blockchain technology and federated learning to enhance data integrity and resilience against attacks, such as Denial of Service (DoS) attacks. The objective of our solution is to bolster the security and operational integrity of IoD systems. Extensive experimentation validates the effectiveness and accuracy of our proposed method. The findings of this research contribute to the evolving field of UAV security, establishing a foundation for proactive defense mechanisms against malicious activities within the IoD ecosystem.
Enhancing Security in Operational Technology: The Role of Multi-Factor Authentication Against Cyber Threats Albandari Alsumayt, Nahla El-Haggar, Majid Alshammari, Fatemah H. Alghamedy, Zeyad AlFawaer Journal of Artificial Intelligence and Technology, 2026 As cyber threats continue to increase in sophistication, the security of Operational Technology (OT) environments has become a paramount priority for organizations in various sectors. OT systems, including Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems, are vital in the functioning of critical infrastructure but often lack robust security due to legacy security weaknesses. This research discusses the implementation of Multi-Factor Authentication (MFA) as a baseline strategy for enhancing the security of such systems. We recognize the distinct cybersecurity issues of OT environments, especially the use of legacy hardware that does not have contemporary security mechanisms. By suggesting an extensive framework for implementing MFA, this research offers a multi-layered system that incorporates knowledge-based, possession-based, and biometric authentication techniques. We further stress the need for role-based access control, ongoing monitoring, and user training to enhance security mechanisms. Using case studies and real-world examples, we show how MFA can be used to counter unauthorized access and increase system resilience. We present actionable recommendations for organizations wishing to deploy MFA, including mitigation strategies that reduce identified vulnerabilities to acceptable levels for critical infrastructure as a foundation of their cybersecurity approach, with the ultimate goal of safeguarding critical infrastructure and sensitive information in a hyper-connected world. Our research not only adds to the body of knowledge but also acts as a guide to deploying stringent security controls in OT networks.
Cutting-Edge DoS Attack Detection in Drone Networks: Leveraging Machine Learning for Robust Security Albandari Alsumayt, Naya Nagy, Shatha Alsharyofi, Resal Alahmadi, Renad Al-Rabie, Roaa Alesse, Noor Alibrahim, Amal Alahmadi, Fatemah H. Alghamedy, Zeyad Alfawaer Sci, 2026 This study aims to enhance the security of unmanned aerial vehicles (UAVs) within the Internet of Drones (IoD) ecosystem by detecting and preventing Denial-of-Service (DoS) attacks. We introduce DroneDefender, a web-based intrusion detection system (IDS) that employs machine learning (ML) techniques to identify anomalous network traffic patterns associated with DoS attacks. The system is evaluated using the CIC-IDS 2018 dataset and utilizes the Random Forest algorithm, optimized with the SMOTEENN technique to tackle dataset imbalance. Our results demonstrate that DroneDefender significantly outperforms traditional IDS solutions, achieving an impressive detection accuracy of 99.93%. Key improvements include reduced latency, enhanced scalability, and a user-friendly graphical interface for network administrators. The innovative aspect of this research lies in the development of an ML-driven, web-based IDS specifically designed for IoD environments. This system provides a reliable, adaptable, and highly accurate method for safeguarding drone operations against evolving cyber threats, thereby bolstering the security and resilience of UAV applications in critical sectors such as emergency services, delivery, and surveillance.
ML and DL Models for Stroke Prediction from Bio-Signals: A Systematic Review and Bibliometric Analysis May Issa Aldossary, Fatemah H. Alghamedy, Dina A. Alabbad, Renad A. Alnuaim, Maimonah S. Altaweel, Reem A. H. Alshami, Haya A. Alzahim, Shahad F. Alotaibi, Sumayh S. Aljameel, Areej Almalki, Sunday O. Olatunji Hightech and Innovation Journal, 2025 Strokes continue to be a primary reason for disability and death around the globe. Annually, over 12.2 million new strokes occur, which necessitates the development of early detection and intervention tools to reduce the potential harm. This systematic review and bibliometric analysis aim to review and visualize recent advances in predicting stroke or post-stroke effects using bio-signals, either with machine learning (ML) or deep learning (DL). The included studies were published between 2016 and 2024. A comprehensive search of IEEE, PubMed, MDPI, and ScienceDirect databases was performed using keywords related to stroke prediction, machine learning, deep learning, and bio-signals. From an initial pool of 152 studies, 15 studies met the inclusion criteria through the screening process. South Korea contributed the most to publishing studies on stroke prediction using bio-signals. The results show that Electroencephalography (EEG) is the most used bio-signal in the reviewed studies. The sample size ranged from 3 to 4068. The top ten cited journals in the selected literature are high-ranked journals, which indicates the scientific validity of the concept and its potential for dissemination.
EEG-Driven Machine Learning for Stroke Detection in High-Risk Patients Fatemah H. Alghamedy, May Issa Aldossary, Dina A. Alabbad, Reem A. Alshami, Maimonah S. Altaweel, Renad A. Alnuaim, Haya A. Alzahim, Shahad F. Alotaibi, Sumayh S. Aljameel, Sunday Olusanya Olatunji, Arwa H. Alghamdi IEEE Access, 2025 Stroke remains a leading cause of disability and mortality worldwide, highlighting the need for effective tools for early detection and intervention. Recent research has explored the use of bio-signals generated by the human body as indicators of stroke occurrence. Among these, Electroencephalography (EEG) has shown particular promise. EEG-based stroke detection offers a non-invasive, cost-effective, accurate, and portable solution. This paper investigates the use of Machine Learning (ML) techniques with EEG data to detect strokes. Four experimental setups were designed to evaluate different feature engineering methods: using all features, selecting features via a Decision Tree (DT) with varying thresholds, and applying Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for dimensionality reduction. Results indicate that the fourth setup—applying ICA with both AdaBoost and XGBoost—yielded the best performance, achieving an accuracy of 89%, precision of 86%, recall of 100%, F1-score of 92%, and a Matthews Correlation Coefficient (MCC) value of 0.76.
A Study of Android Security Vulnerabilities and Their Future Prospects Albandari Alsumayt, Heba Elbeh, Mohamed Elkawkagy, Zeyad Alfawaer, Fatemah H. Alghamedy, Majid Alshammari, Sumayh S. Aljameel, Sarah Albassam, Shahad AlGhareeb, Khadijah Alamoudi Hightech and Innovation Journal, 2024 Nowadays, smartphones are used for various activities, including checking emails, paying bills, and playing games, which have become essential parts of daily life. Also, IoT devices can be managed and controlled using applications. While applications can provide numerous benefits, they have also led to several security risks, such as theft of data, eavesdropping, compromised data, and denial-of-service attacks. This study examines security breaches, attacks targeting Android system applications, and vulnerabilities present at every layer of the Android architecture. Additionally, the study aims to compare and evaluate various treatment methods to identify their advantages and disadvantages. Furthermore, the study aims to examine Android's architecture for weaknesses that might lead to app vulnerabilities and potential attacks. To achieve the objectives of this study, a comprehensive analysis of security breaches and attacks targeting Android system applications will be conducted. Various treatment methods will be compared and evaluated through rigorous examination. Additionally, Android's architecture will be thoroughly examined to identify potential weaknesses and vulnerabilities. The analysis will focus on identifying the security risks associated with the use of applications on smartphones and IoT devices. The vulnerabilities present at every layer of the Android architecture will also be analyzed. Furthermore, the advantages and disadvantages of various treatment methods will be assessed. The findings of this study will reveal the various security risks, vulnerabilities, and potential weaknesses present in Android system applications and the Android architecture. The advantages and disadvantages of different treatment methods will also be highlighted. This study contributes to the development of more precise and robust security measures for Android, aiming to mitigate security breaches, attacks, and vulnerabilities. By identifying weaknesses and vulnerabilities, this study provides valuable insights for improving the overall security of Android system applications. Doi: 10.28991/HIJ-2024-05-03-020 Full Text: PDF
Boundaries and Future Trends of ChatGPT Based on AI and Security Perspectives Albandari Alsumayt, Zeyad M. Alfawaer, Nahla El-Haggar, Majid Alshammari, Fatemah H. Alghamedy, Sumayh S. Aljameel, Dina A. Alabbad, May Issa Aldossary Hightech and Innovation Journal, 2024 In decades, technology and artificial intelligence have significantly impacted aspects of life. One noteworthy development is ChatGPT, an AI-based model that has created a revolution and attracted attention from researchers, academia, and organizations in a short period of time. Experts predict that ChatGPT will continue advancing, bringing about a leap in artificial intelligence. It is believed that this technology holds the potential to address cybersecurity concerns, protect against threats and attacks, and overcome challenges associated with our increasing reliance on technology and the internet. This technology may change our lives in productive and helpful ways, from the interaction with other AI technologies to the potential for enhanced personalization and customization to the continuing improvement of language model performance. While these new developments have the potential to enhance our lives, it is our responsibility as a society to thoroughly examine and confront the ethical and societal impacts. This research delves into the state of ChatGPT and its developments in the fields of artificial intelligence and security. It also explores the challenges faced by ChatGPT regarding privacy, data security, and potential misuse. Furthermore, it highlights emerging trends that could influence the direction of ChatGPT's progress. This paper also offers insights into the implications of using ChatGPT in security contexts. Provides recommendations for addressing these issues. The goal is to leverage the capabilities of AI-powered conversational systems while mitigating any risks. Doi: 10.28991/HIJ-2024-05-01-010 Full Text: PDF
DualEye-FeatureNet: A dual-stream feature transfer framework for multi-modal ophthalmic image classification Muhammad Shafiq, Fan Quanrun, Fatemah H. Alghamedy, Waeal J.Obidallah IEEE Access, 2024 Eye diseases are a significant health issue due to the drastic increase in the use of digital gadgets and mobile devices, making early detection and intervention essential for effective treatment. In recent times, the multimodal imagery fusion approach has garnered growing interest in automated disease detection for various eye disorders (Glaucoma, Cataracts, Diabetic Retinopathy (DR), Myopia, and Macular Degeneration (MD)). In this work, we propose a reliable, multi-modal, automated eye disease classification method using a novel fully automated DL framework called DualEye-FeatureNet. The proposed framework is a dual-stream deep learning architecture that combines complementary deep neural network models (DarkNet53 and ResNet101) with standard clustering techniques (Fuzzy C-means and K-means) to exploit features from OCT images and fundus images. The integrated form of two parallel stream of features is fed to the unique 3D-CNN for discrimination of eye diseases classification. Experimental results demonstrate the potential of the dual-stream model in capturing not only structural elements but also the spatial relationships of features in complex OCT and fundus images, effectively improving both performance and generalizability over state-of-art individual-modality approaches. The multi-modal ophthalmic image classification accuracies of 94% for Glaucoma, 92% of Cataracts, 95% for DR, 93% of Myopia and 91% for MD were obtained, respectively. The proposed architecture overcomes the limitation of single-modality diagnosis and significantly emerges as a novel fully automated deep learning framework.
Unlocking a Promising Future: Integrating Blockchain Technology and FL-IoT in the Journey to 6G Fatemah H. Alghamedy, Nahla El-Haggar, Albandari Alsumayt, Zeyad Alfawaer, Majid Alshammari, Lobna Amouri, Sumayh S. Aljameel, Sarah Albassam IEEE Access, 2024 The rapid advancement of technology has set higher standards for the next generation of wireless communication networks, known as 6G. These networks go beyond the simple task of connecting devices and aim to establish a self-sustaining system within society. One of the key factors in achieving this goal is the integration of AI services and apps through the Internet of Things (IoT), which will be made possible with the support of 6G technology. The advancement of artificial intelligence (AI) will play a crucial role in enhancing the protocols, architectures, and operations of 6G networks. To achieve collaborative AI in IoT applications, Federated Learning (FL) has emerged as a popular method. FL enables AI training without the need for data sharing, ensuring privacy and security. However, FL also faces challenges, such as the presence of malicious data and the risk of single-point failure. To address these concerns, blockchain technology (BCT) offers a secure and efficient solution. By leveraging blockchain, these issues can be effectively tackled, providing a reliable framework for implementing FL-IoT applications.
