Abstract
Cybersecurity has become a critical concern in today’s digital landscape, with organizations facing a constant stream of sophisticated threats. Neural networks, a subset of artificial intelligence, have shown great promise in predicting and managing these threats by analyzing large volumes of data, identifying anomalies, and making decisions based on patterns. This article explores the role of neural networks in enhancing cybersecurity, focusing on their applications in threat detection, intrusion prevention, and risk management. It discusses different neural network architectures, challenges in implementation, and future directions in the field of cybersecurity.
All articles published in the American Journal of Artificial Intelligence and Neural Networks (AJAINN) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Under this license:
-
Authors retain full copyright of their work.
-
Readers are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially.
-
Proper credit must be given to the original author(s) and the source, a link to the license must be provided, and any changes made must be indicated.
This open licensing ensures maximum visibility and reusability of research while maintaining author rights.