A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile...

Area of Science:

• Computer Science
• Cybersecurity
• Wireless Communication

Background:

• Advancements in wireless communication and electronic devices generate massive data volumes.
• Increasingly complex mobile network topologies heighten the risk of security breaches.
• Security concerns impede the adoption of smart mobile applications and services.

Purpose of the Study:

• To examine current challenges in mobile network security.
• To explore the role of artificial intelligence (AI) in enhancing mobile data protection.
• To discuss machine learning (ML) and deep learning (DL) techniques for secure mobile environments.

Main Methods:

• Review of existing mobile network security threats.
• Analysis of AI-based security models for data secrecy, integrity, and authenticity.
• Discussion of various machine learning (ML) and deep learning (DL) techniques.

Main Results:

• AI-based security models are crucial for ensuring data protection in complex networks.
• Identified open security challenges include unauthorized network scanning and fraudulent links.
• ML and DL techniques offer potential solutions for various cybersecurity threats.

Conclusions:

• Developing novel approaches is essential for high electronic data security in mobile networks.
• The potential for improving mobile network security is extensive.
• AI is a key enabler for addressing evolving cybersecurity challenges.