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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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2.

001-es BibID:BIBFORM122023
035-os BibID:(WoS)000774599800010 (Scopus)85128336939
Első szerző:Abdulsada, Ayad I.
Cím:Privacy preserving scheme for document similarity detection / Ayad I. Abdulsada, Salah Al-Darraji, Dhafer G. Honi
Dátum:2022
ISSN:1300-0632
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Turkish Journal of Electrical Engineering and Computer Sciences. - 30 : 3 (2022), p. 609-628. -
További szerzők:Al-Darraji, Salah Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD)
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3.

001-es BibID:BIBFORM121816
035-os BibID:(Scopus)85110330327
Első szerző:Abdulsada, Ayad I.
Cím:Efficient multi-keyword similarity search over encrypted cloud documents / Ayad I. Abdulsada, Dhafer G. Honi, Salah Al-Darraji
Dátum:2021
ISSN:2502-4752 2502-4760
Megjegyzések:Many organizations and individuals are attracted to outsource their data into remote cloud service providers. To ensure privacy, sensitive data should be encrypted before being hosted. However, encryption disables the direct application of the essential data management operations like searching and indexing. Searchable encryption is a cryptographic tool that gives users the ability to search the encrypted data while being encrypted. However, the existing schemes either serve a single exact search that loss the ability to handle the misspelled keywords or multi-keyword search that generate very long trapdoors. In this paper, we address the problem of designing a practical multi-keyword similarity scheme that provides short trapdoors and returns the correct results according to their similarity scores. To do so, each document is translated into a compressed trapdoor. Trapdoors are generated using key based hash functions to ensure their privacy. Only authorized users can issue valid trapdoors. Similarity scores of two textual documents are evaluated by computing the Hamming distance between their corresponding trapdoors. A robust security definition is provided together with its proof. Our experimental results illustrate that the proposed scheme improves the search efficiency compared to the existing schemes. Furthermore, it shows a high level of performance.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Cloud computing
Multi-keywords ranking
search
Privacy preserving
Searchable encryption
Simhash
Megjelenés:Indonesian Journal of Electrical Engineering and Computer Science. - 23 : 1 (2021), p. 510-518. -
További szerzők:Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Al-Darraji, Salah
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4.

001-es BibID:BIBFORM121815
035-os BibID:(WoS)000913143400001 (Scopus)85119048409
Első szerző:Al-Darraji, Salah
Cím:Employee Attrition Prediction Using Deep Neural Networks / Salah Al-Darraji, Dhafer G. Honi, Francesca Fallucchi, Ayad I. Abdulsada, Romeo Giuliano, Husam A. Abdulmalik
Dátum:2021
ISSN:2073-431X
Megjegyzések:Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
deep learning
machine learning
attrition prediction
Megjelenés:Computers. - 10 : 11 (2021), p. 1-11. -
További szerzők:Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Fallucchi, Francesca Abdulsada, Ayad I. Giuliano, Romeo Abdulmalik, Husam A.
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5.

001-es BibID:BIBFORM122022
Első szerző:Alshuwaili, Dhafer Gheni Honi (Informatics)(PhD)
Cím:Privacy Preserving Image Matching Scheme with Aggregated Local Descriptors / Dhafer G. Honi, Husam A. Abdulmalik, Ayad I. Abdulsada, Salah Al-Darraji
Dátum:2022
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Megjelenés:IEEE Conference Publication. - p. 1-7. -
További szerzők:Abdulmalik, Husam A. Abdulsada, Ayad I. Al-Darraji, Salah
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