Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912757052342272 |
|---|---|
| author | Zhou, Yuyang Cheng, Guang Du, Kang Chen, Zihan Zhao, Yuyu |
| author_facet | Zhou, Yuyang Cheng, Guang Du, Kang Chen, Zihan Zhao, Yuyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_21051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense Zhou, Yuyang Cheng, Guang Du, Kang Chen, Zihan Zhao, Yuyu Cryptography and Security Artificial Intelligence Networking and Internet Architecture 68 F.2.2; I.2.8 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. |
| title | Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense |
| topic | Cryptography and Security Artificial Intelligence Networking and Internet Architecture 68 F.2.2; I.2.8 |
| url | https://arxiv.org/abs/2412.21051 |