用大模型实现云安全主动防御，自动应对多种拒绝服务攻击。
Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense
- 用大模型分析数据并推理，动态生成防御策略。
- 实测对三种DoS攻击防御效果优于现有方法。
- 能从经验中自我进化，无需重新训练即可适应新攻击。
云计算技术快速发展，应用日益广泛，但其组件多样复杂，给云安全带来挑战，尤其面对如拒绝服务（DoS）等高级攻击。大语言模型（LLMs）在语言理解、数据分析、任务推理和代码生成等方面的能力为安全智能提供新思路。本文提出新型防御架构LLM-PD，可主动应对云网络中的多种DoS威胁。该架构通过全面数据分析与序列推理高效决策，并动态生成与部署可执行的防御机制。此外，它能基于以往交互经验灵活自演化，适应新攻击场景而无需额外训练。案例研究显示，相较于其他现有方法，LLM-PD在防御有效性和效率方面表现突出。
原文摘要 · Abstract (English)
The rapid evolution of cloud computing technologies and the increasing number of cloud applications have provided numerous benefits in our daily lives. However, the diversity and complexity of different components pose a significant challenge to cloud security, especially when dealing with sophisticated and advanced cyberattacks such as Denial of Service (DoS). Recent advancements in the large language models (LLMs) offer promising solutions for security intelligence. By exploiting the powerful capabilities in language understanding, data analysis, task inference, action planning, and code generation, we present LLM-PD, a novel defense architecture that proactively mitigates various DoS threats in cloud networks. LLM-PD can efficiently make decisions through comprehensive data analysis and sequential reasoning, as well as dynamically create and deploy actionable defense mechanisms. Furthermore, it can flexibly self-evolve based on experience learned from previous interactions and adapt to new attack scenarios without additional training. Our case study on three distinct DoS attacks demonstrates its remarkable ability in terms of defense effectiveness and efficiency when compared with other existing methods.
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