What question did this study set out to answer?

The aim is to improve API security in 5G networks by employing AI models for threat detection.

April 24, 2026Open Access

Intelligent Threat Defense Mechanisms for 5G APIs

Key Points

The aim is to improve API security in 5G networks by employing AI models for threat detection.
Developed two AI models: Long Short-Term Memory (LSTM) and Reinforcement Learning (RL).
Created synthetic traffic that simulates normal and attack scenarios (DDoS, brute force, SQL injection).
Evaluated the effectiveness of AI models against traditional rule-based security systems.
LSTM and RL models achieved approximately 95% detection accuracy, outpacing traditional methods (58% accuracy).
AI models detected more threats and generated fewer false alerts compared to rule-based systems.
Reinforcement Learning excelled at identifying unknown or evolving attacks.

Abstract

As 5G Standalone Core networks grow, Application Programming Interface (APIs) have become a key part of how network systems talk to each other. They allow different functions to share data and complete tasks quickly. However, this also makes them targets for attacks. 5G Standalone Core networks rely on Service-Based Architecture (SBA), where network functions communicate through exposed APIs. These APIs are attractive targets for cyberattacks because they are externally accessible, handle sensitive control-plane operations, and exchange structured data using Hypertext Transfer Protocol version 2 (HTTP/2) and JavaScript Object Notation (JSON) protocols. Most older security tools work using fixed rules, which cannot always detect new or changing threats. This study aimed to fix that gap by using Artificial Intelligence to make API security smarter. Two AI models were tested: Long Short-Term Memory (LSTM), which learns from past traffic and Reinforcement Learning (RL), which learns by adapting to network behavior. Both were used to assess API traffic and assign a real-time risk score. Synthetic traffic was created using Python, including both normal API calls and different types of attacks like Distributed Denial-of-Service (DDoS), brute force, and Structured Query Language (SQL) injection. The results show that both LSTM and RL models were better than traditional rule-based systems. They found more threats, gave fewer false alerts, and responded faster. RL was especially strong at handling unknown or changing attacks. Experimental results show that the proposed LSTM and RL models achieved approximately 95% detection accuracy, significantly outperforming the static rule-based baseline model, which achieved 58% accuracy. The results demonstrate the effectiveness of adaptive AI-based security mechanisms for detecting evolving API threats. This research shows that AI can help protect 5G APIs in a smarter and more flexible way. It can support telecom networks by making threat detection faster, more accurate, and ready for future challenges.

Intelligent Threat Defense Mechanisms for 5G APIs

Key Points

Abstract

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