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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Hiba Zuhair
Al-Nahrain University
Computer Science & IT Electrical & Electronics Engineering

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Data security using random dynamic salting and AES based on master-slave keys for Iraqi dam management system Hussam J. Ali; Talib M. Jawad; Hiba Zuhair
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1018-1029

Abstract

In the present time, dam management is considered one of the important challenges for e-government in Iraq, becuase it needs information technology infrastructure, data integrity and protection of user privacy against Internet threats that render such vital infrastructure ineffective. This struggle between the proposed dam management system (DMS) and a multi-tier secure model specifically for the Fallujah dam (and generally for all dams) which is addressed in this paper as a case study. To do this, a relational database design will discuss the development of a multi-tier secure model for integration of the dam management framework with its functions. This paper will discusse encryption and decryption of the dam data using the advanced encryption standard (AES) algorithm with derived keys via PBKDF2 and RNG sequences generator and Slave key for salting protection. The experimental results and analysis on the speed of encryption/decryption process, entropy value, plain text sensitivity, key sensitivity, keyspaceanalysis and histogram analysis will prove the the proposed scheme can impede the known attacks like brute force attacks, statistical and differential.Thus, the encryption scheme can be implemented on the proposed DMS and any other information system, as the implementation which will be presented in the results.
Deep learning in phishing mitigation: a uniform resource locator-based predictive model Hamzah Salah; Hiba Zuhair
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3227-3243

Abstract

To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model’s architecture encompasses pre-processing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learning-based model of convolution neural network with bi-directional long short-term memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.
Co-Authors Hamzah Salah Hussam J. Ali Talib M. Jawad