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.

翻译：信息安全正面临日益严峻的挑战，传统防护手段难以应对复杂多变的威胁。近年来，大语言模型作为一种新兴智能技术，在信息安全领域展现出广阔的应用前景。本文聚焦大语言模型在信息安全中的关键作用，系统梳理了其在恶意行为预测、网络威胁分析、系统漏洞检测、恶意代码识别以及密码算法优化等方面的应用进展，并探讨了其在提升安全防护性能方面的潜力。基于神经网络与Transformer架构，本文分析了大语言模型的技术基础及其在自然语言处理任务中的优势。研究表明，大语言模型的引入有助于提升安全系统的检测精度并降低误报率。最后，本文总结了当前应用成果，指出其在模型透明度、可解释性及场景适应性等方面仍面临挑战，需进一步探索模型结构优化与泛化能力提升，以实现更智能化、精准化的信息安全防护体系。