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1.

001-es BibID:BIBFORM122126
Első szerző:Abduljabbar, Zaid Ameen
Cím:MAC-Based Symmetric Key Protocol for Secure Traffic Forwarding in Drones / Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Junchao Ma, Mustafa A. Al Sibahee, Mustafa S. Khalefa, Dhafer G. Honi
Dátum:2022
ISBN:978-3-031-15100-2
Tárgyszavak:Természettudományok Matematika- és számítástudományok előadáskivonat
könyvrészlet
Megjelenés:6th EAI International Conference, FABULOUS 2022, Virtual Event, May 4, 2022, Proceedings / eds. University of Zagreb. - p. 16-36. -
További szerzők:Nyangaresi, Vincent Omollo Ma, Junchao Sibahee, Mustafa A. Al Khalefa, Mustafa S. Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD)
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2.

001-es BibID:BIBFORM121767
035-os BibID:(WoS)000769957000001 (Scopus)85124754177
Első szerző:Abduljabbar, Zaid Ameen
Cím:Provably Secure and Fast Color Image Encryption Algorithm Based on S-Boxes and Hyperchaotic Map / Zaid Ameen Abduljabbar, Iman Qays Abduljaleel, Junchao Ma, Mustafa A. Al Sibahee, Vincent Omollo Nyangaresi, Dhafer G. Honi, Ayad I. Abdulsada, Xianlong Jiao
Dátum:2022
ISSN:2169-3536
Megjegyzések:The World Wide Web is experiencing a daily increase in data transmission because of developments in multimedia technologies. Consequently, each user should prioritize preventing illegal access of this data by encrypting it before moving it over the Internet. Numerous color image encryption schemes have been developed to protect data security and privacy, indifferent to the computation cost. However, most of these schemes have high computational complexities. This research proposes a fast color image scrambling and encryption algorithm depending on different chaotic map types and an S-box that relies on a hyperchaotic map principle. The first step involves converting color image values from decimal representation to binary representation in the scrambling stage by changing the location of the bits according to a proposed swapping algorithm. Next, in the second scrambling stage, the same process occurs after returning color image values from binary representation to decimal representation and generating an S-box with the assistance of two types of chaotic map,namely,a 2D Zaslavsky map and a 3D Hénon map.Thus,this S-box is relied upon to swap the locations of the pixels in the color image. The encryption procedure begins with the production of three key matrices using a hybrid technique that employs two low-complexity types of chaotic map, namely, a 1D Logistic map and a 3D Hénon map, followed by an XORed as a lightweight process between each key generated for the three matrices and the corresponding red, green, and blue image channels. According to the findings, the proposed scheme demonstrates the most efficiency in terms of lowering the computational cost and shows its effectiveness against a wide range of cryptographic attacks.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Pixel mixing
chaos
fast encryption
S-box
Zaslavsky map
color image
Megjelenés:IEEE Access. - 10 (2022), p. 26257-26270. -
További szerzők:Abduljaleel, Iman Qays Ma, Junchao Sibahee, Mustafa A. Al Nyangaresi, Vincent Omollo Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD) Abdulsada, Ayad I. Jiao, Xianlong
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3.

001-es BibID:BIBFORM122021
Első szerző:Ali, Ali Hasan (matematikus)
Cím:Provably Efficient and Fast Technique for Determining the Size of a Brain Tumor in T1 MRI Images / Ali Hasan Ali, Hend Muslim Jasim, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Samir M. Umran, Junchao Ma, Dhafer G. Honi
Dátum:2024
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
artificial intelligence
Megjelenés:International Conference on Artificial Intelligence in Information and Communication (ICAIIC) / eds. IEEE. - p. 608-613. -
További szerzők:Jasim, Hend Muslim Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Umran, Samir M. Ma, Junchao Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD)
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4.

001-es BibID:BIBFORM122018
Első szerző:Alshuwaili, Dhafer Gheni Honi (Informatics)(PhD)
Cím:Towards Fast Edge Detection Approach for Industrial Products / Dhafer G. Honi, Ali Hasan Ali, Zaid Ameen Abduljabbar, Junchao Ma, Vincent Omollo Nyangaresi, Keyan Abdul-Aziz Mutlaq, Samir M. Umran
Dátum:2023
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Megjelenés:2022 IEEE 21st International Conference on Ubiquitous Computing and Communications. - - (2023), p. 239-244. -
További szerzők:Ali, Ali Hasan (1989-) (matematikus) Abduljabbar, Zaid Ameen Ma, Junchao Nyangaresi, Vincent Omollo Mutlaq, Keyan Abdul-Aziz Umran, Samir M.
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5.

001-es BibID:BIBFORM126908
035-os BibID:(Scopus)85207485730
Első szerző:Ghrabat, Mudhafar Jalil Jassim
Cím:Utilizing Machine Learning for the Early Detection of Coronary Heart Disease / Mudhafar Jalil Jassim Ghrabat, Siamand Hassan Mohialdin, Luqman Qader Abdulrahman, Murthad Hussein Al-Yoonus, Zaid Ameen Abduljabbar, Dhafer G. Honi, Vincent Omollo Nyangaresi, Iman Qays Abduljaleel, Husam A. Neamah
Dátum:2024
ISSN:2241-4487 1792-8036
Tárgyszavak:Műszaki tudományok Villamosmérnöki tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Engineering Technology & Applied Science Research. - 14 : 5 (2024), p. 17363-17375. -
További szerzők:Mohialdin, Siamand Hassan Abdulrahman, Luqman Qader Al-Yoonus, Murthad Hussein Abduljabbar, Zaid Ameen Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD) Nyangaresi, Vincent Omollo Abduljaleel, Iman Qays Neamah, Husam A. (1990-) (mérnök)
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6.

001-es BibID:BIBFORM132223
Első szerző:Jassim Ghrabat, Mudhafar Jalil
Cím:Effective SMOTE boost with deep learning for IDC identification in whole-slide images / 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, Husam A. Neamah
Dátum:2025
ISSN:1932-6203
Megjegyzések: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.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Plos One. - 20 : 9 (2025), p. 1-34. -
További szerzők:Ghaib, Arkan A. Al-Hossenat, Auhood Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Ma, Junchao Aldarwish, Abdulla J. Y. Abduljaleel, Iman Qays Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD) Neamah, Husam A. (1990-) (mérnök)
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7.

001-es BibID:BIBFORM127905
035-os BibID:(WOS)001315548100061 (Scopus)85206998869
Első szerző:Najem, Duaa Fadhel
Cím:Low-Complexity and Secure Clustering-Based Similarity Detection for Private Files / Duaa Fadhel Najem, Nagham Abdulrasool Taha, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Junchao Ma, Dhafer G. Honi
Dátum:2024
ISSN:2217-8309 2217-8333
Megjegyzések:Detection of the similarity between files is a requirement for many practical applications, such as copyright protection, file management, plagiarism detection, and detecting duplicate submissions of scientific articles to multiple journals or conferences. Existing methods have not taken into consideration file privacy, which prevents their use in many delicate situations, for example when comparing two intellectual agencies' files where files are meant to be secured, to find file similarities. Over the last few years, encryption protocols have been developed with the aim of detecting similar files without compromising privacy. However, existing protocols tend to leak important data, and do not have low complexity costs. This paper addresses the issue of computing the similarity between two file collections belonging to two entities who desire to keep their contents private.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
File similarity
privacy
similarity detection
Megjelenés:TEM Journal-Technology Education Management Informatics. - 13 : 3 (2024), p. 2341-2349. -
További szerzők:Taha, Nagham Abdulrasool Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Ma, Junchao Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD)
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8.

001-es BibID:BIBFORM127839
035-os BibID:(Scopus)85206998869 (WoS)001315548100061
Első szerző:Najem, Duaa Fadhel
Cím:Low-Complexity and Secure Clustering-Based Similarity Detection for Private Files / Duaa Fadhel Najem, Nagham Abdulrasool Taha, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Junchao Ma, Dhafer G. Honi
Dátum:2024
ISSN:2217-8309 2217-8333
Megjegyzések:Detection of the similarity between files is a requirement for many practical applications, such as copyright protection, file management, plagiarism detection, and detecting duplicate submissions of scientific articles to multiple journals or conferences. Existing methods have not taken into consideration file privacy, which prevents their use in many delicate situations, for example when comparing two intellectual agencies' files where files are meant to be secured, to find file similarities. Over the last few years, encryption protocols have been developed with the aim of detecting similar files without compromising privacy. However, existing protocols tend to leak important data, and do not have low complexity costs. This paper addresses the issue of computing the similarity between two file collections belonging to two entities who desire to keep their contents private. We propose a clustering-based approach that achieves 90% accuracy while significantly reducing the execution time. The protocols presented in this study are much more efficient than other secure protocols, and the alternatives are slower in terms of similarity detection for large file sets. Our system achieves a high level of security by using a vector space model to convert the files into vectors and by applying Paillier encryption to encrypt the elements of the vector separately, to protect privacy. The study uses the application of the Porter algorithm to the vocabulary set. Using a secure cosine similarity approach, a score for similar files was identified and the index of the similarity scores is returned to the other party, rather than the similar files themselves. The system is strengthened by using clustering for files, based on the k-means clustering technique, which makes it more efficient for large file sets.
Tárgyszavak:Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
File similarity
privacy
similarity detection
Megjelenés:TEM Journal-Technology Education Management Informatics. - 13 : 3 (2024), p. 2341-2349. -
További szerzők:Taha, Nagham Abdulrasool Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Ma, Junchao Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD)
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9.

001-es BibID:BIBFORM122701
035-os BibID:(Scopus)85198346104 (WoS)001272466300018
Első szerző:Nyangaresi, Vincent Omollo
Cím:Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme / Vincent Omollo Nyangaresi, Zaid Ameen Abduljabbar, Keyan Abdul-Aziz Mutlaq, Salim Sabah Bulbul, Junchao Ma, Abdulla J. Y. Aldarwish, Dhafer G. Honi, Mustafa A. Al Sibahee, Husam A. Neamah
Dátum:2024
ISSN:2045-2322
Megjegyzések:Advancements in cloud computing, flying ad?hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as sidechanneling are thwarted. We formally analyze the security of our protocol using the Burrows-AbadiNeedham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de?synchronization, eavesdropping, session hijacking, forgery and side?channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state?of?the?art protocols.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Authentication
Biometrics
Fuzzy commitment
Security
Privacy
Efficiency
Hamming distance
Smart city
Megjelenés:Scientific Reports. - 14 : 1 (2024), p. 1-17. -
További szerzők:Abduljabbar, Zaid Ameen Mutlaq, Keyan Abdul-Aziz Bulbul, Salim Sabah Ma, Junchao Aldarwish, Abdulla J. Y. Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD) Al Sibahee, Mustafa A. Neamah, Husam A. (1990-) (mérnök)
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10.

001-es BibID:BIBFORM122019
035-os BibID:(WoS)000900395100001 (Scopus)85144866037
Első szerző:Nyangaresi, Vincent Omollo
Cím:Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes / Vincent Omollo Nyangaresi, Zaid Ameen Abduljabbar, Keyan Abdul-Aziz Mutlaq, Junchao Ma, Dhafer G. Honi, Abdulla J. Y. Aldarwish, Iman Qays Abduljaleel
Dátum:2022
ISSN:2076-3417
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Applied Sciences-Basel. - 12 : 24 (2022), p. 1-24. -
További szerzők:Abduljabbar, Zaid Ameen Mutlaq, Keyan Abdul-Aziz Ma, Junchao Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD) Aldarwish, Abdulla J. Y. Abduljaleel, Iman Qays
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11.

001-es BibID:BIBFORM121814
Első szerző:Nyangaresi, Vincent Omollo
Cím:A Symmetric Key and Elliptic Curve Cryptography-Based Protocol for Message Encryption in Unmanned Aerial Vehicles / Vincent Omollo Nyangaresi, Hend Muslim Jasim, Keyan Abdul-Aziz Mutlaq, Zaid Ameen Abduljabbar, Junchao Ma, Iman Qays Abduljaleel, Dhafer G. Honi
Dátum:2023
ISSN:2079-9292
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Electronics (Switzerland). - 12 : 17 (2023), p. 1-20. -
További szerzők:Jasim, Hend Muslim Mutlaq, Keyan Abdul-Aziz Abduljabbar, Zaid Ameen Ma, Junchao Abduljaleel, Iman Qays Alshuwaili, Dhafer Gheni Honi (1991-) (Informatics)(PhD)
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