Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Saida, El Mendili
Unknown Affiliation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Systematic review for attack tactics, privacy, and safety models in big data systems Chaymae, Majdoubi; Youssef, Gahi; Saida, El Mendili
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1234-1250

Abstract

This systematic review explores cyberattack tactics, privacy concerns, and safety measures within big data systems, focusing on the critical challenges of maintaining data security in today's complex digital environments. The review begins by categorizing various cyberattacks, laying the groundwork for understanding the threats to big data. It identifies key vulnerabilities that compromise privacy and safety, and examines the ethical implications of these issues. The role of artificial intelligence in enhancing security defenses is highlighted as a crucial aspect of mitigating these threats. Additionally, a comparative assessment of regulatory frameworks such as GDPR, NIST, and ISO 27001 is provided, emphasizing the importance of legal and compliance considerations in data protection. The review concludes by proposing a structured approach to cyberattack detection and processing, advocating for strategies that address both technical vulnerabilities and regulatory requirements, followed by a critical discussion on the usability of previous methods for mobile security, highlighting adaptability and performance, discussing explainability and Gen AI adoption. This work offers valuable insights for researchers, practitioners, and policymakers, contributing to the ongoing discourse on cybersecurity in the big data era.
Financial sentiment analysis of tweets based on deep learning approach Issam, Aattouchi; Mounir, Ait Kerroum; Saida, El Mendili; Fatna, El Mendili
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1759-1770

Abstract

The volume of unstructured texts has increased dramatically in recent years due to the internet and the digitization of information and literature. This onslaught of data will only grow, and it will come from new and unusual sources. Thus, it will be necessary to develop new and inventive approaches and tools to process and make sense of this data. Investors in the financial markets can now get information faster than ever before thanks to the expansion of communication channels, in addition to the online availability of news and reports in text format through providers like Reuters and Bloomberg. This contains a plethora of information that is often overlooked by financial market data. In order to measure the sentiment of a text, predictive and deductive methods are applied, these methods aim at extrapolating new feautures from big data. The main objective of this study is to create and test a new system capable of predicting finance and non-finance related tweets. The convolutional neural network (CNN) and latent dirichlet allocation (LDA) algorithms are used in the proposed approche. The suggested model's correctness is tested against a benchmark financial dataset, and the results demonstrate that with a database of 1,000,000 data points, our model is 99% accurate.
Co-Authors Chaymae, Majdoubi Fatna, El Mendili Issam, Aattouchi Mounir, Ait Kerroum Youssef, Gahi