Network Security Threats and Mitigation Strategies in Mobile Networks: A Machine Learning Perspective

Network Security Threats and Mitigation Strategies in Mobile Networks: A Machine Learning Perspective

연구 분야: Infrastructure

학회: 2025 International Conference on Pervasive Computational Technologies (ICPCT)

Mobile phones are integral to our lives and provide a gateway for our communication and online services through mobile networks across the globe. But now mobile networks are becoming more and more woven into the fabric of everyday life, and the risk of mobile security breaches has soared. From data breaches to malware attacks, we are all at the risk of losing money and the data of most people and organizations. To counter to these security threats a variety of classic solutions have been used in sequence such as firewalls and encryption. But the increasing sophistication of threats has rendered more and more of these alternatives impractical. Well, that is where machine learning (ML) techniques come into play. Although AWS security services are not directly related to ML, it can help analyze large amounts of data and identify trends, which can detect and prevent any potential attacks in advance. Thus, our research work encourages the use of ML techniques for detecting and mitigating threats in network security applied in mobile networks. Next, we will gather and evaluate the network traffic information and teach the ML model to identify the malware patterns and anomalies. You are using that model to predict and stop threats in real-time. Finally, we will discuss how ML-based approaches can be used to dynamically adjust security policies and manage resources in mobile networks. This method aims to enhance the overall security of mobile networks by detecting and preventing potential security threats. This will not only stop potential attacks but will also reduce the time and the resources needed for incident response. We hope to further secure and refine the integrity of every aspect of the mobile network ecosystem through this research.