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All Journal The Indonesian Journal of Computer Science
Alajali, walaa
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Control the Sensitivity of the Encryption Key to Ensure the Security of Big Data Wafaa Ali; Alajali, Walaa; Abdulrahman D. Alhusaynat
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4660

Abstract

With the increasing technological development that has led to the complexity of big data systems, the importance lies in the challenge that ensures the security of sensitive information. Encryption is one of the basic methods used to protect data, hence the importance of the encryption key and its sensitivity, which play a vital role in the strength of encryption. Encryption sensitivity is the simple change in the encryption key that produces a very different encrypted text. This study is concerned with methods of controlling the sensitivity of the encryption key and its effect on the strength of encryption of big data. This context is in line with the nature of cloud data and the focus on the attacks that this data suffers from, such as brute-force attacks and statistical attacks. The research discusses the components that make up the encryption key, logistics maps, and chaos. The results reached by the study proved the merit of the research in terms of accuracy 1015 and appropriate key sensitivity 2128. This study discussed future challenges and the possibility of using artificial intelligence algorithms and adaptive security algorithms and solving quantum encryption problems.
Multi-Level Stress Classification Using the Electroencephalogram Based on Mental Load Tasks Alajali, Walaa
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.5022

Abstract

Stress is considered one of the major global health issues contributing to cardiovascular disease and depression among other disorders. This study examines the amounts of stress and performance on tasks using electroencephalogram (EEG) data and machine learning. Raw EEG data is preprocessed to remove noise and segment epochs. Empirical Mode Decomposition (EMD) is followed by Butterfly Optimization Algorithm (BOA) for feature extraction and dimensionality reduction. Five machine learning classifiers (SVM, Naive Bayes, Random Forest, KNN, Decision Tree) classify four levels of stress (neutral, low, medium, and high) based on cognitive load during two tasks: the Stroop color-word task and an arithmetic task. Results indicate the Naive Bayes classifiers for the Stroop and arithmetic tasks had accuracies of 98.82% and 98.87% respectively, while the SVM classifier achieved 99.02% accuracy for both tasks combined. Such results attest to the growing interest and application of machine learning on EEG data for mental health monitoring and the possible enhancement of task performance. .
Co-Authors Abdulrahman D. Alhusaynat Wafaa Ali