# Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense

arXiv: https://arxiv.org/abs/2412.21051 · HTML: https://mlsanity.com/paper/2412.21051/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 30 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Large Language Models for Telecom: Forthcoming Impact on the Industry](https://mlsanity.com/paper/2308.06013/) (2023)
- [Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices](https://mlsanity.com/paper/2306.14263/) (2023)
- [Large Language Models for Networking: Applications, Enabling Techniques, and Challenges](https://mlsanity.com/paper/2311.17474/) (2023)
- [Multitask-based Evaluation of Open-Source LLM on Software Vulnerability](https://mlsanity.com/paper/2404.02056/) (2024)
- [LLM in the Shell: Generative Honeypots](https://mlsanity.com/paper/2309.00155/) (2023)
- [CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions](https://mlsanity.com/paper/2408.09304/) (2024)

## Built on by (most cited first, 4 of 4)

- [From LLMs to MLLMs to Agents: A Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem](https://mlsanity.com/paper/2506.15170/) (2025)
- [AI Native Asset Intelligence](https://mlsanity.com/paper/2605.09115/) (2026)
- ["Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations](https://mlsanity.com/paper/2604.15588/) (2026)
- [PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety](https://mlsanity.com/paper/2609.28197/) (2026)

## Abstract (arXiv)

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.