LLMs now guard cloud networks like expert security teams
LLM-PD introduces an intelligent cloud defense system that uses LLMs to automatically detect and neutralize cyber threats through proactive analysis and adaptive response mechanisms.
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https://arxiv.org/abs/2412.21051v1
🔍 Original Problem:
Cloud networks face increasingly complex security threats that traditional defense methods struggle to handle effectively. Current solutions lack intelligent guidance and require extensive retraining for new scenarios.
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🛡️ Solution in this Paper:
→ LLM-PD architecture employs five specialized LLM agents working in harmony: collector, analyzer, decision-maker, deployer, and feedback-giver.
→ The collector gathers and standardizes security data from multiple tools across the cloud network.
→ The analyzer assesses system status and evaluates risks on a 0-10 scale based on threat scope, impact, and duration.
→ The decision-maker breaks down complex defense tasks and develops strategies through sequential reasoning.
→ The deployer executes defense strategies by either using existing mechanisms or generating new defense scripts.
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🎯 Key Insights:
→ LLMs can effectively handle complex security scenarios without extensive retraining
→ Hierarchical task decomposition enables handling multiple threats simultaneously
→ Self-evolution through feedback loops improves defense efficiency over time
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📊 Results:
→ Achieved 88.8% survival rate against SYN Flooding attacks
→ Maintained 92.1% effectiveness against SlowHTTP attacks
→ Demonstrated 93.5% success rate against Memory DoS attacks
→ Reduced average defense steps from 16.3 to 7.53 through experience accumulation
