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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)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM117946
035-os BibID:	(WOS)000565865300009
Első szerző:	Nsaif, Mohammed (informatics)
Cím:	Detection and Prevention Algorithm of DDoS Attack Over the IOT Networks / Mohammed Ridha Nsaif, Mohammed Falah Abbood, Abbas Fadhil Mahdi
Dátum:	2020
ISSN:	2217-8309 2217-8333
Megjegyzések:	Traffic classification is a crucial aspect for Software- Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game and ICMP. The performance of a number of Machine Learning techniques is compared and the results are reported. As part of future work, we will incorporate classification into the power consumption model towards achieving further advances in this research area.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Machine learning classification dataset SDN
Megjelenés:	TEM Journal-Technology Education Management Informatics. - 9 : 3 (2020), p. 899-906. -
További szerzők:	Mohammed, Falah Abbood Abbas Fadhil Mahdi
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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