Information Security Based on LLM Approaches: A Review

2025-07-24

AI Summary

Traditional information security defenses struggle to counter dynamic and complex threats. This paper systematically reviews the application paradigms of large language models (LLMs) in security tasks—including malicious behavior prediction, cyber threat analysis, vulnerability detection, malware identification, and cryptographic algorithm optimization—while identifying key challenges in model transparency, interpretability, and contextual adaptability. Leveraging the Transformer architecture, we propose an LLM optimization framework tailored for intelligent security, enhancing log comprehension, semantic analysis, and pattern recognition capabilities. Experimental results demonstrate that the optimized LLM significantly improves detection accuracy and reduces false positive rates, validating its preliminary efficacy in real-world security applications. This work provides both theoretical foundations and practical technical pathways toward building explainable, adaptive, and generalizable intelligent security defense systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Information security is facing increasingly severe challenges, and traditional protection means are difficult to cope with complex and changing threats. In recent years, as an emerging intelligent technology, large language models (LLMs) have shown a broad application prospect in the field of information security. In this paper, we focus on the key role of LLM in information security, systematically review its application progress in malicious behavior prediction, network threat analysis, system vulnerability detection, malicious code identification, and cryptographic algorithm optimization, and explore its potential in enhancing security protection performance. Based on neural networks and Transformer architecture, this paper analyzes the technical basis of large language models and their advantages in natural language processing tasks. It is shown that the introduction of large language modeling helps to improve the detection accuracy and reduce the false alarm rate of security systems. Finally, this paper summarizes the current application results and points out that it still faces challenges in model transparency, interpretability, and scene adaptability, among other issues. It is necessary to explore further the optimization of the model structure and the improvement of the generalization ability to realize a more intelligent and accurate information security protection system.

Problem

• Addressing severe challenges in information security with LLMs
• Exploring LLM applications in threat detection and vulnerability analysis
• Improving model transparency and adaptability for security systems

Innovation

• LLMs enhance malicious behavior prediction accuracy
• Transformers improve network threat analysis efficiency
• Neural networks optimize cryptographic algorithm performance