Enterprise AI Analysis: Automating NIDS Rule Labeling with LLMs vs. Machine Learning

An OwnYourAI.com breakdown of the paper "Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models" by Nir Daniel et al.

The Core Enterprise Problem: The SOC Analyst's Dilemma

SOC analysts are the frontline defenders of an enterprise's digital assets. Yet, they face a deluge of alerts from NIDS tools like Snort. Each alert, generated by a specific rule, must be investigated, understood, and mapped to a potential adversary's tactics and techniques (TTPs) using a framework like MITRE ATT&CK. This process is highly manual, requires deep expertise, and leads to significant challenges:

• Alert Fatigue: The sheer volume of alerts makes it impossible to investigate everything, leading to missed threats.
• The Explainability Gap: Many NIDS rules are cryptic, lacking clear context about the specific attack they detect. This slows down response times.
• Talent Scarcity: Experienced security analysts who can quickly decipher these rules are rare and expensive.

The research explores how AI can bridge this gap. Below is a conceptual model of how AI transforms this workflow.

Methodology Showdown: AI-Powered NIDS Labeling

The paper evaluates two distinct AI paradigms for this task. Understanding their strengths and weaknesses is key to designing an effective enterprise solution.

Approach 1: LLMs for Contextual Reasoning

This approach leverages the vast knowledge and reasoning capabilities of models like OpenAI's ChatGPT, Anthropic's Claude, and Google's Gemini. Through sophisticated "prompt engineering," the LLMs were tasked to read a Snort rule and determine the corresponding MITRE ATT&CK technique. The key takeaway is that performance is heavily dependent on the quality of the prompt, with the best results coming from providing both a list of possible techniques (contextual guidance) and a few examples of correctly labeled rules (In-Context Learning or ICL).

Approach 2: ML Models for High-Precision Automation

The second approach uses a more traditional, supervised learning pipeline. The researchers used the LLMs to generate an initial labeled dataset, which then trained specialized ML models (like Support Vector Machines - SVM) to perform the classification task at scale. These models convert the NIDS rule text into numerical features (using TF-IDF) and learn to predict labels with high accuracy.

The results are stark. The SVM model, trained by an LLM, achieved an F1-score of 0.87 for techniques and a remarkable 0.92 for tactics. This level of accuracy is far superior to the LLMs' direct performance, highlighting ML's strength in production environments where precision is paramount.

The OwnYourAI Hybrid Framework: The Best of Both Worlds

The paper's findings don't declare a single winner; they illuminate a strategic path forward. Neither LLMs nor ML alone is the complete solution. The optimal enterprise strategy is a custom-built hybrid system that leverages the unique strengths of each technology. We call this the AI-Augmented Threat Intelligence Cycle.

Calculating the Enterprise Value: An Interactive ROI Model

Implementing a hybrid AI solution yields tangible business value by automating manual work, accelerating threat response, and empowering your existing security team. Use our interactive calculator, based on the efficiency gains demonstrated in the research, to estimate the potential ROI for your organization.

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