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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Saba Alaa Abdulwahhab
Tikrit University
Computer Science & IT Engineering

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An overview of number theory research unit variant development security Saba Alaa Abdulwahhab; Qasim Mohammed Hussein; Imad Fakhri Al-Shaikhli
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1164-1173

Abstract

Number theory research unit (NTRU) become the most important of securi-ty in recent, with its modification of their variant, this paper search of the literature and A number of studies have examined the in public key variant development and security. In general, prior work is limited to a subset of public key increasing complexity but the benefits of speed up encryption/ decryption have not been fully established. So this paper will be the basis for those who want to develop and find proposed solutions for new studies of the NTRU algorithm. This paper aims to develop a framework to investigate the NTRU development, had been discovered that despite its development over the years and even its acceptance in round three of post quantum cryptograph, then found that limit study in the new scope of quantum facility and the ability of hybrid of new study.
Comparison between ensemble and linear methods for website phishing detection Saba Hussein Rashid; Saba Alaa Abdulwahhab; Farah Amer Abdulaziz
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp681-694

Abstract

In the current digitalized world, the notion of cybersecurity has become crucial in everyday life, and the issue of privacy takes the leading role in the technological agenda of the global community. One such social engineering attack that is currently prevalent is phishing, which is a common technique used by cybercriminals to intercept sensitive data. Despite the presence of certain limitations which can restrict its usefulness, machine learning (ML) has evolved into an interesting approach to identify phishing attacks. Cloud ML is an effective solution that uses cloud computing solutions to create, train, and deploy models that provide a faster and more accurate result as well as support large datasets. This paper compares the ensemble method of Amazon SageMaker’s AutoML tool, AutoGluon, with the linear method of SageMaker’s linear learner algorithm for website phishing detection. Key factors examined include training techniques, training time, batch transform time, endpoint prediction time, and model accuracy. The results demonstrate that while AutoGluon outperforms linear learner in terms of accuracy and prediction speed, linear learner is faster in training and batch transform processes.
Co-Authors Farah Amer Abdulaziz Imad Fakhri Al-Shaikhli Qasim Mohammed Hussein Saba Hussein Rashid