This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities, such as prompt injection, insecure output handling, data poisoning, DDoS attacks, and adversarial instructions. We delve into mitigation strategies to protect these models, providing a comprehensive look at potential attack scenarios and prevention techniques. Furthermore, we evaluate the performance of 42 LLM models in cybersecurity knowledge and hardware security, highlighting their strengths and weaknesses. We thoroughly evaluate cybersecurity datasets for LLM training and testing, covering the lifecycle from data creation to usage and identifying gaps for future research. In addition, we review new strategies for leveraging LLMs, including techniques like Half-Quadratic Quantization (HQQ), Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), Quantized Low-Rank Adapters (QLoRA), and Retrieval-Augmented Generation (RAG). These insights aim to enhance real-time cybersecurity defenses and improve the sophistication of LLM applications in threat detection and response. Our paper provides a foundational understanding and strategic direction for integrating LLMs into future cybersecurity frameworks, emphasizing innovation and robust model deployment to safeguard against evolving cyber threats.

翻译：本文全面回顾了通过生成式人工智能和大语言模型（LLMs）实现网络安全的未来前景。我们探讨了LLM在多个领域的应用，包括硬件设计安全、入侵检测、软件工程、设计验证、网络威胁情报、恶意软件检测及钓鱼检测。本文概述了LLM的演进历程及其当前状态，重点关注GPT-4、GPT-3.5、Mixtral-8x7B、BERT、Falcon2和LLaMA等模型的进展。分析还涉及LLM的脆弱性，例如提示注入、不安全输出处理、数据投毒、分布式拒绝服务攻击（DDoS）及对抗性指令。我们深入探讨了保护这些模型的缓解策略，全面审视了潜在攻击场景与防御技术。此外，我们评估了42个LLM模型在网络安全知识和硬件安全方面的表现，揭示了其优势与不足。我们全面评估了用于LLM训练与测试的网络安全数据集，涵盖从数据创建到使用的全生命周期，并指出了未来研究的空白。同时，我们回顾了利用LLM的新策略，包括半二次量化（HQQ）、基于人类反馈的强化学习（RLHF）、直接偏好优化（DPO）、量化低秩适配器（QLoRA）及检索增强生成（RAG）等技术。这些洞见旨在增强实时网络安全防御能力，并提升LLM在威胁检测与响应中的应用复杂度。本文为将LLM集成至未来网络安全框架提供了基础理解与战略方向，强调通过创新与稳健的模型部署来防范不断演变的网络威胁。