Leveraging Machine Learning for Advanced Cybersecurity in Next-Generation Networks
DOI:
https://doi.org/10.56294/digi2025181Keywords:
Cyber security , Data analysis , Cybercrime, ML algorithms , Threats, Network securityAbstract
Introduction; As technology advances rapidly, cybersecurity has become a pressing global issue. The growing complexity of cyber threats presents significant challenges to individuals, organizations, and governments alike.
Objective; Cybersecurity tools are essential for detecting, monitoring, and mitigating these risks, ensuring data security, preventing unauthorized access, and protecting sensitive information. However, conventional methods often fall short in addressing the sophistication of modern cyber-attacks.
Method; Machine learning (ML) has emerged as a transformative approach to strengthening cybersecurity. By analyzing vast datasets, ML algorithms can identify anomalies and predict threats with higher precision. When integrated into cybersecurity frameworks, ML enhances defenses against issues like data breaches, identity theft, and system intrusions.
Result; This research focuses on utilizing ML to develop and optimize cybersecurity models tailored for enterprise ICT systems. Additionally, it highlights the increasing demand for experts capable of designing and implementing ML-driven security solutions.
Conclusion; By exploring emerging trends, this study underscores the pivotal role of ML in fortifying digital security globally.
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Copyright (c) 2025 Md. Alimul Haque, Kushboo Mishra, B. K. Mishra (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
