Intelligent Classification System for Cybersecurity Alerts Based on Large Language Models

This article introduces an intelligent classification system for cybersecurity alerts built using Large Language Models (LLMs), aiming to solve the "alert fatigue" problem in Security Operations Centers (SOCs). Through the natural language understanding and reasoning capabilities of LLMs, the system automatically classifies network intrusion alerts, reduces false positive rates, and generates structured incident reports, thereby improving SOC operational efficiency and threat response capabilities.

Modern enterprise SOCs are responsible for monitoring, detecting, and responding to network threats. However, the complexity of attack methods leads to thousands of alerts needing to be processed daily, making "alert fatigue" a common problem. Traditional rule-based static alert classification methods struggle to adapt to new attack types, with false positive rates as high as over 90%, which severely drains the efficiency of security teams.

The system consists of three key components:

1. Alert Ingestion Layer: Receives raw alerts from various security devices, performs standardization and format conversion;
2. Intelligent Analysis Engine: The core module, which uses LLMs to deeply analyze alert content, extract semantic information, understand attack context, and adapt to specific environments through few-shot learning or fine-tuning;
3. Classification and Report Generation: Automatically classifies alert priorities (Urgent/High/Medium/Low) and generates structured reports containing attack types, impact scope, and disposal recommendations. The core design concept is to apply the semantic understanding and reasoning capabilities of LLMs to the field of cybersecurity, breaking through the limitations of traditional feature matching.

Compared to traditional solutions, the system has significant advantages:

• Reduced False Positive Rate: LLMs understand semantic context, effectively distinguishing between real attacks and false positives;
• Improved Response Speed: Automated classification ensures immediate attention to high-priority events, shortening the response window;
• Knowledge Precipitation and Reuse: Structured reports accumulate into a security knowledge base, helping to improve team capabilities;
• Scalability: The generalization ability of LLMs adapts to new attack types without frequent updates to the rule base.

The system is applicable to multiple SOC scenarios:

• Daily Monitoring: Filters massive alert noise and highlights key events;
• Emergency Response: Quickly generates preliminary analysis reports to assist decision-making;
• Compliance Auditing: Provides complete processing records and reports to meet regulatory requirements;
• Operational Optimization: Identifies opportunities for strategy improvement through alert trend analysis. This system frees security teams from tedious screening and allows them to focus on threat analysis and defense optimization.

Practical deployment faces challenges:

• Data Privacy: Sensitive alert information needs to be protected; local deployment or federated learning are feasible paths;
• Model Hallucination: Confidence assessment and manual review are required to ensure output quality;
• Real-time Performance: Model inference efficiency needs to be optimized to balance accuracy and performance. Future Outlook: Combine multimodal analysis (traffic, logs, endpoint behavior) with reinforcement learning to achieve higher-level automation and become a core component of next-generation security operation platforms.

The Network-Traffic-Triage-System project is an innovative application of AI in the field of cybersecurity, providing new ideas for solving "alert fatigue". It combines LLM capabilities with SOC needs, effectively improving operational efficiency. For enterprises seeking to optimize security operations, this intelligent solution is worth in-depth exploration and practice.