Iryna Zavushchak

Work place: Institute of Computer Sciences and Information Technologies, Department of Information Systems and Networks. Lviv Polytechnic National University, Lviv, Ukraine

E-mail: iryna.i.zavushchak@lpnu.ua

Website: https://orcid.org/ 0000-0002-5371-8775

Research Interests:

Biography

Iryna Zavushchak obtained a master's degree in the specialty "Artificial Intelligence Systems" and obtained the qualification "Computer Systems Analyst" of the Lviv Polytechnic National University in 2015. Obtained the degree of candidate of technical sciences, majoring in mathematics and software support of computing machines and systems, from the National University of Lviv Polytechnic in 2019. Currently works as an associate professor at the Department of Information Systems and Networks of the Lviv Polytechnic National University.

Author Articles
The Impact of Artificial Intelligence on Cybersecurity and Data Protection

By Iryna Zavushchak

DOI: https://doi.org/10.5815/ijwmt.2025.04.05, Pub. Date: 8 Aug. 2025

The escalating complexity of cybersecurity threats necessitates advanced technological solutions to protect digital infrastructures. This study explores the application of Autoencoder neural networks, a deep learning model, for anomaly detection in network traffic, aiming to enhance real-time identification of cyberattacks. Using the CICIDS2017 dataset, which encompasses diverse attack types such as Distributed Denial of Service (DDoS) and infiltration, the Autoencoder was trained to detect deviations from normal traffic patterns based on reconstruction errors. The model was optimized through preprocessing, feature selection, and hyperparameter tuning, achieving strong performance metrics including precision, recall, F1-score, accuracy, and ROC-AUC. Despite its effectiveness in distinguishing normal and malicious traffic, challenges arose in detecting stealthy attacks like slow brute-force attempts. These results underscore the Autoencoder's potential in cybersecurity frameworks and highlight opportunities for improvement through adaptive thresholds and hybrid models. This study contributes to advancing AI-driven anomaly detection, promoting proactive defense against evolving cyber threats.

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