Zaid Ameen Abduljabbar
@uobasrah.edu.iq
Computer Science, Education College for Pure Science
University of Basrah
ZAID AMEEN ABDULJABBAR received the
bachelor's and master's degrees in computer sci-
ence from Basrah University, Iraq, in 2002 and
2006, respectively, and the Ph.D. degree in
computer engineering from the Department of
Computer Science and Technology, Huazhong
University of Science and Technology, China,
in 2017. His research interests include cloud
security, searchable encryption systems, similarity
measures, the Internet of Things, secure compu-
tation, biometric, and soft computing. He has published regular articles
in many IEEE International Conferences and High-quality articles in SCI
journals, and has got the Best Paper Award and published in the 11th Inter-
national Conference on Green, Pervasive, and Cloud Computing (GPC16),
Xian, China, in May 2016. He has always served as a Reviewer for several
prestigious journals, and has served as the PC Chair/PC member for more
than 20 international conferences.
EDUCATION
Ph.D in computer applied technology
RESEARCH INTERESTS
Cloud Computing, IoT, and Information Security
Scopus Publications
Scopus Publications
Ahmed Abed Mohammed, Zainab Amin Al-Sulami, Mustafa M. Abd Zaid, Zaid Ameen Abduljabbar, Mustafa Aldarbandee, Hamzah Jaber, Dhafer G. Honi, Laszlo Szathmary, Vincent Omollo Nyangaresi, Ali Hasan Ali,et al.
Elsevier BV
Haider Malik, Jun Feng, Pingping Shao, and Zaid Ameen Abduljabbar
Elsevier BV
Shahad Amjed Hamed, Khawla Hussein Ali, and Zaid Ameen Abduljabbar
Springer Nature Switzerland
Haider Malik, Jun Feng, Pingping Shao, and Zaid Ameen Abduljabbar
Elsevier BV
Nima M. Nima, Ali A. Yassin, Hamid Ali Abed AL-Asadi, Zaid Ameen Abduljabbar, and Vincent Omollo Nyangaresi
Springer Nature Switzerland
Khtam AL-Meyah, Zainab Bager Dahoos, Kholud A. karoon, Zaid Ameen Abduljabbar, Zahraa Abdullah Ali, Zaid Alaa Hussien, Abdulla J. Y. Aldarwis, and Vincent Omollo Nyangaresi
Springer Nature Switzerland
Iman Fareed Khazal, Arkan A. Ghaib, Asmaa Shareef, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Iman Qays Abduljaleel, Abdulla J. Y. Aldarwish, Ali Hasan Ali, and Zaid Alaa Hussien
Springer Nature Switzerland
Mustafa A. Al Sibahee, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Jianqiang Li, Chengwen Luo, Alladoumbaye Ngueilbaye, Junchao Ma, Yijing Huang, Jin Zhang, and Husam A. Neamah
Institute of Electrical and Electronics Engineers (IEEE)
Basheer A. Hassoon, Shengwu Xiong, Mushtaq A. Hasson, and Zaid Ameen Abduljabbar
Springer Science and Business Media LLC
Enas W. Abood, Ali A. Yassin, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, and Ali Hasan Ali
Elsevier BV
Haider Malik, Jun Feng, Mohammed Abdallah, Jiru Zhang, Pingping Shao, and Zaid Ameen Abduljabbar
Elsevier BV
Asmaa Shareef, Salah Al-darraji, Suhaib Al-Ansarry, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ali Hasan Ali, and Husam A. Neamah
Elsevier BV
Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ahmed Ali Ahmed, Junchao Ma, Mustafa A. Al Sibahee, Mohammed Abdulridha Hussain, Zaid Alaa Hussien, Ali Hasan Ali, Abdulla J. Y. Aldarwish, and Husam A. Neamah
Springer Science and Business Media LLC
Mudhafar Jalil Jassim Ghrabat, Arkan A. Ghaib, Auhood Al-Hossenat, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Junchao Ma, Abdulla J. Y. Aldarwish, Iman Qays Abduljaleel, Dhafer G. Honi, and Husam A. Neamah
Public Library of Science (PLoS)
Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolutional Neural Network (CNN) technology to detect areas impacted by Invasive Ductal Carcinoma (IDC) in whole slide pictures. The trials used a dataset of 162 individuals with IDC, split into training (113 photos) and testing (49 images) groups. Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. The results highlight the effectiveness of the created model in properly detecting IDC-affected tissue areas, showing great promise for improving breast cancer diagnosis and treatment planning. We surpassing other models as such, CNN, VGG19, ResNet50.
Keyan Abdul-Aziz Mutlaq, Mushtaq A. Hasson, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Mustafa A. Al Sibahee, Junchao Ma, Samir M. Umran, Ali Hasan Ali, Abdulla J.Y. Aldarwish, and Husam A. Neamah
Elsevier BV
Nouralhuda Ali Abdulsamad, Ali A. Yassin, Zaid Ameen Abduljabbar, Mohammed S. Hashim, and Vincent Omollo Nyangaresi
Engineering, Technology & Applied Science Research
The vast majority of strokes are caused by an unexpected occlusion of the blood vessels that supply the brain and the heart arteries. Early detection of the many warning symptoms of stroke can help reduce the severity of the stroke and save the patient's life. Although researchers have proposed a variety of diagnostic methods to detect this disease, the methods currently in use still need further improvement. In this paper, we propose an effective methodology that utilizes a hard voting classifier based on three Machine Learning (ML) models, namely, Random Forest (RF), K-Nearest Neighbors (KNN), and Extra Trees Classifier (ETC). First, a series of data quality improvement procedures were performed using the Synthetic Minority Oversampling Technique (SMOTE) approach for data balancing to ensure an unbiased training process without majority class dominance. Next, we divided the dataset into two parts, a training part and a testing part, and these data were fed to the models used. In the last phase, we implemented four ML algorithms to evaluate their effectiveness and then selected the three most effective models for integration into our proposed hard voting classifier. The hard voting outperformed the results of modern studies with an accuracy of 97.48%, a precision of 0.9802, a recall of 0.9691, and an F1 score of 0.9747. Furthermore, we applied K-fold cross-validation (K=10), which systematically partitions the dataset into multiple subsets, preventing overfitting and providing a robust estimate of model performance across different data splits, where a mean accuracy of 97.1% was achieved.
Murtadha Alazzawi, Saad Alfadhli, Ahmed Shammari, Zaid Abduljabbar, and Vincent Nyangaresi
ScopeMed
Abstract: The Fifth Generation (5G) networks have enabled the development of smart cities in which massive amounts of data are collected, stored and disseminated. The ultimate objective of these smart cities is to cut costs and improve security performance. In this environment, Internet of Vehicles (IoV) helps connect vehicles, pedestrians, control rooms, and some roadside infrastructure. Owing to the insecure nature of the communication channel utilized in IoV to exchange information, it is important to develop practical techniques to preserve data confidentiality and privacy. To this end, numerous security solutions have been proposed over the recent past. Unfortunately, most of these authentication techniques have security flaws which endanger the transmitted data, while some of them are highly inefficient. To address these gaps, we present a Lightweight Authentication Scheme for the Internet of Vehicles (IoV) based on 5G technology (LAIOV-5G).The security analysis carried out demonstrates that LAIOV-5G mitigates numerous potential attacks that threaten the IoV communication in a smart city environment. In addition, the performance analysis of LAIOV-5G verifies its effectiveness and efficiency.
Muhammad Amir, Jamshaid Ul Rahman, Ali Hasan Ali, Ali Raza, Zaid Ameen Abduljabbar, and Husam A. Neamah
Elsevier BV
Mustafa Jumaah, Ali A. Yassin, Zaid Ameen Abduljabbar, Muwafaq Jawad, Vincent Omollo Nyangaresi, and Ali Hassan Ali
Engineering, Technology & Applied Science Research
Obfuscated malware poses a significant threat to personal and IoT devices, and traditional detection methods often face significant challenges and weaknesses in their capabilities and performance. This study proposes a malware detection approach using Machine Learning (ML) algorithms and a soft voting ensemble technique, enhanced by the Pearson's correlation coefficient for feature selection on the CIC-MalMem-2022 dataset. It addresses data imbalances with the Synthetic Minority Oversampling Technique (SMOTE) method and employs various ML classifiers. The results demonstrate improved accuracy, precision, and recall in malware detection compared to single classifiers and traditional methods. The research model is evaluated using a confusion matrix and evaluation metrics, and achieves 99.99% accuracy rate, 99.99% classification rate, 99.99% precision rate, 99.99% recall rate and 99.99% F1 score, surpassing the results of previous studies. These results indicate that the combination of feature selection and ensemble learning can significantly improve the efficiency and security of high-performance malware prediction systems, paving the way for advanced threat mitigation strategies.
Muwafaq Jawad, Ali Yassin, Hamid asadi, Zaid Abduljabbar, Vincent Nyangares, Zaid Hussien, and Husam Neamah
ScopeMed
The Internet of Health Things (IoHT) is a network of healthcare devices, software, and systems that enable remote monitoring and healthcare services by gathering real-time health data through sensors. Despite its significant benefits for modern smart healthcare, IoHT faces growing security challenges due to the limited processing power, storage capacity, and self-defense capabilities of its devices. While blockchain-based authentication solutions have been developed to leverage tamper-resistant decentralized designs for enhanced security, they often require substantial computational resources, increased storage, and longer authentication times, hindering scalability and time efficiency in large-scale, time-critical IoHT systems. To address these challenges, we propose a novel four-phase authentication scheme comprising setup, registration, authentication, and secret construction phases. Our scheme integrates chaotic-based public key cryptosystems, a Light Encryption Device (LED) with a 3-D Lorenz chaotic map algorithm, and blockchain-based fog computing technologies to enhance both efficiency and scalability. Simulated on the Ethereum platform using Solidity and evaluated with the JMeter tool, the proposed scheme demonstrates superior performance, with a computational cost reduction of 40% compared to traditional methods like Elliptic Curve Cryptography (ECC). The average latency for registration is 1.25 ms, while the authentication phase completes in just 1.50 ms, making it highly suitable for time-critical IoHT applications. Security analysis using the Scyther tool confirms that the scheme is resistant to modern cyberattacks, including 51% attacks and hijacking, while ensuring data integrity and confidentiality. Additionally, the scheme minimizes communication costs and supports the scalability of large-scale IoHT systems. These results highlight the proposed scheme’s potential to revolutionize secure and efficient healthcare monitoring, enabling real-time, tamper-proof data management in IoHT environments.
Keyan Abdul-Aziz Mutlaq, Vincent Omollo Nyangaresi, Mohd Adib Omar, Zaid Ameen Abduljabbar, Junchao Ma, Mustafa A. Al Sibahee, Abdulla J. Y. Aldarwish, and Ali Hasan Ali
Public Library of Science (PLoS)
Smart grids collect real-time power consumption reports that are then forwarded to the utility service providers over the public communication channels. Compared with the traditional power grids, smart grids integrate information and communication technologies, cyber physical systems, power generation and distribution domains to enhance flexibility, efficiency, transparency and reliability of the electric power systems. However, this integration of numerous heterogeneous technologies and devices increases the attack surface. Therefore, a myriad of security techniques have been introduced based on technologies such as public key cryptosystems, blockchain, bilinear pairing and elliptic curve cryptography. However, majority of these protocols have security challenges while the others incur high complexities. Therefore, they are not ideal for some of the smart grid components such as smart meters which are resource-constrained. In this paper, a protocol that leverages on digital certificates, signatures, elliptic curve cryptography and blockchain is developed. The formal verification using Real-Or-Random (ROR) model shows that the derived session keys are secure. In addition, semantic security analysis shows that it is robust against typical smart grid attacks such as replays, forgery, privileged insider, side-channeling and impersonations. Moreover, the performance evaluation shows that our protocol achieves a 17.19% reduction in the computation complexity and a 46.15% improvement in the supported security and privacy features.
Ali Noori Gatea, Haider Sh. Hashim, Hamid Ali Abed Al-Asadi, Didem Kivanc Tureli, Zaid Ameen Abduljabbar, and Vincent Omollo Nyangaresi
Engineering, Technology & Applied Science Research
Mobile Ad hoc Networks (MANETs) are infrastructure-independent wireless networks where nodes communicate directly or through relays without a central base station. Routing protocols employed in MANETs face numerous challenges due to their limited resources. Cross-layer optimization is fundamental to conserving energy and achieving quality of service parameters. However, reducing end-to-end diversity conflicts with power consumption, creating a problem when trying to improve network lifetime. In this work, a Lifetime Enhancement Routing (LER) protocol, which selects the most efficient path to the destination using residual energy and cost exchange metrics, is proposed. LER primarily reduces node overutilization and load to prolong the network lifetime. The proposed MANET performance optimization technique is Gaussian clustering algorithm with one of the deep learning (RNN) techniques as a combined technique. The simulation results show that the proposed protocol significantly reduced energy consumption and augmented the ability to send data through the best path available in the network with a high efficiency of up to 92%.
Murtadha Al-Maliki, Wala’a Hussein, Mustafa Moosa Qasim, Zaid Ameen Abduljabbar, Ahmed Ali Ahmed, and Ali Hasan Ali
Engineering, Technology & Applied Science Research
Iraq's industry has gone through various transformation phases and has seen tremendous growth during the recent years. To sustain such growth, the infrastructure should be highly efficient. Fiber optic technology is a main component in the networks because it provides high bandwidth and high speed, thus providing support for current and emerging technologies. To the best of our knowledge, various research works carried out in Iraq so far have not touched on the point of effective improvement in the performance of the fiber optic communication system. The concept behind this research is the design of a Radio over Fiber system using the Optisystem simulator, focusing on how to improve the performance of a multi-core fiber optic communication system by improving the transmission capacity and enhancing the reception system to raise the quality of the received signal and obtain a lower bit error rate. The simulation results showed that there was much enhancement in the quality of transmission, reducing the bit error rate by 10 times in comparison with previous systems while providing better signal clarity. These improvements are in line with recent advances in optical fiber technology used in similar studies globally.
Thekaa Ali Kadhim, Zaid Ameen Abduljabbar, Hamid Ali Abed AL-Asadi, Vincent Omollo Nyangaresi, Zahraa Abdullah Ali, and Iman Qays Abduljaleel
MDPI AG
Intelligent precision agriculture incorporates a number of Internet of Things (IoT) devices and drones to supervise agricultural activities and surroundings. The collected data are then forwarded to processing centers to facilitate crucial decisions. This can potentially help optimize the usage of agricultural resources and thwart disasters, enhancing productivity and profitability. To facilitate monitoring and decision, the smart devices in precision agriculture must exchange massive amounts of data across the open wireless communication channels. This inadvertently introduces a number of vulnerabilities, exposing the collected data to numerous security and privacy threats. To address these issues, massive security solutions have been introduced to secure the communication process in precision agriculture. However, most of the current security solutions either fail to offer perfect protection or are inefficient. In this paper, a scheme deploying efficient cryptographic primitives such as hashing, exclusive OR and random number generators is presented. We utilize the Burrows–Abadi–Needham (BAN) logic to demonstrate the verifiable security of the negotiated session keys. In addition, we execute an extensive semantic analysis which reveals the robustness of our scheme against a myriad of threats. Moreover, comparative performance evaluations demonstrate its computation overheads and energy consumption efficiency.
Zahraa Sh. Alzaidi, Ali A. Yassin, Zaid Ameen Abduljabbar, and Vincent Omollo Nyangaresi
Engineering, Technology & Applied Science Research
Authentication of vehicles and users, integrity of exchanged messages, and privacy preservation are essential features in VANETs. VANETs are used to collect information on road conditions, vehicle location and speed, and traffic congestion data. The open exchange of information within VANETs poses serious security threats. Furthermore, existing schemes have higher communication and computational costs, making them incompatible with resource-constrained VANET applications. This study proposes a multifactor authentication and privacy-preserving security scheme for VANETs based on blockchain and fog computing to meet all these requirements. The proposed scheme uses fingerprints and Quick Response (QR) codes as a multifactor to authenticate vehicle users and fog-cloud computing techniques to reduce the computational burden on RSUs and improve service quality and resilience. Additionally, the scheme synchronizes a consistent ledger across all RSUs using blockchain technology to store and distribute vehicle authentication statuses. Through a thorough comparison with relevant current protocols, the scheme shows a much-reduced computing expense and communication burden in situations with high vehicle density within a timeframe of 6.3846 ms and 544 bytes for communication costs. In addition, the proposed scheme demonstrates a successful balance between efficacy and complexity, protecting confidentiality, anonymous authentication, and ensuring integrity and conditional tracking. Formal and informal security analysis showed that the proposed scheme is more reliable, practical, and secure against many hostile attacks, such as modification attacks, 51% attacks, Sybil attacks, and MITM attacks.