Characterizing Cyber Networks Using Large Language Models: A Novel Approach to Threat Hunting

Core Concepts

Large language models (LLMs) like BERT can be used to create behavioral embeddings from network traffic logs, offering a promising new approach to threat hunting by identifying anomalous activity based on deviations from established behavioral patterns.

Abstract

Bibliographic Information:

Hartsock, A., Pereira, L.M., & Fink, G. (2024). Towards Characterizing Cyber Networks with Large Language Models. arXiv preprint arXiv:2411.07089v1.

Research Objective:

This paper explores the application of large language models (LLMs) for threat hunting in cybersecurity, aiming to characterize network entities and their behaviors by analyzing Zeek network traffic logs.

Methodology:

The researchers developed CLEM (Cyber Log Embeddings Model), a tool that leverages a BERT model trained on Zeek connection logs. CLEM analyzes network traffic in overlapping time windows, deliberately overfitting to each window to capture specific behavioral patterns. The model generates embeddings for IP addresses and connections, which are then dimensionally reduced using UMAP for visualization and clustering. The effectiveness of CLEM's clustering is evaluated using the Adjusted Rand Index (ARI) by comparing it to expert-annotated labels.

Key Findings:

CLEM successfully clustered network entities based on their behavior, showing significant correlation with expert-derived classifications. The model demonstrated an ARI of 0.82 for connection embeddings in the PNNL dataset, indicating a strong agreement between CLEM's unsupervised clustering and expert knowledge.

Main Conclusions:

The research suggests that LLMs like BERT hold significant promise for threat hunting applications. By generating behavioral embeddings from network logs, CLEM can identify anomalous activity that deviates from established patterns, providing valuable insights for cybersecurity professionals.

Significance:

This research introduces a novel approach to threat hunting that leverages the power of LLMs for behavioral analysis. CLEM's ability to identify anomalies based on deviations from learned patterns offers a valuable tool for detecting and mitigating cyber threats.

Limitations and Future Research:

The study acknowledges the need for further research with larger and more diverse datasets to validate CLEM's effectiveness in real-world scenarios. Future work will focus on developing a "Network Storm Tracker" to visualize network behavior over time and improve the interpretation of embedding movements for threat hunters.

Stats

The PNNL dataset achieved an ARI of 0.82 when comparing CLEM's clustering of connection embeddings to expert labels.
The ACI dataset, while using real devices, involved simulated attacks and lacked human activity, resulting in a lower ARI of 0.06 for IP address embeddings.

Quotes

"Threat hunting is an open-ended cybersecurity exploration to detect abnormal behaviors that automated tools cannot easily find."
"CLEM is a threat hunting analysis tool that uses BERT to derive embeddings from Zeek connection (conn) logs to automatically classify entities in a computer network by their behavior."
"We believe embeddings capture intentional behaviors, but we have not yet been able to prove this conclusively."

Key Insights Distilled From

Towards Characterizing Cyber Networks with Large Language Models

by Alaric Harts... at arxiv.org 11-12-2024

https://arxiv.org/pdf/2411.07089.pdf

Deeper Inquiries

How might the integration of CLEM with other threat intelligence platforms enhance its ability to detect and respond to sophisticated cyberattacks?

Integrating CLEM with other threat intelligence platforms could significantly enhance its ability to detect and respond to sophisticated cyberattacks in several ways:

Contextual Enrichment: CLEM primarily focuses on behavioral analysis of network entities. Integrating it with threat intelligence platforms can provide contextual information about observed IP addresses, domains, or files. For instance, if CLEM flags an unusual port connection, cross-referencing with threat intelligence could reveal if that port is associated with known malware or command-and-control servers. This added context can help distinguish truly malicious behavior from benign anomalies.

Improved Anomaly Detection: Threat intelligence platforms often contain information about emerging threats, attack patterns, and Indicators of Compromise (IOCs). Integrating this data into CLEM can enhance its anomaly detection capabilities. For example, CLEM could leverage threat intelligence to identify suspicious network traffic patterns that might otherwise go unnoticed, such as communication with newly discovered botnet infrastructure.

Automated Response Actions: Integration with threat intelligence platforms can enable automated response actions based on CLEM's findings. For instance, if CLEM detects a device exhibiting behavior consistent with a known malware infection, the integrated platform could automatically trigger actions like isolating the infected device, blocking malicious traffic, or initiating further investigation.

Proactive Threat Hunting: CLEM's ability to identify behavioral anomalies can be leveraged for proactive threat hunting. By integrating with threat intelligence platforms, security analysts can use CLEM to search for specific patterns or behaviors associated with emerging threats within their network, even if those threats haven't been directly observed yet.

Enhanced Visualization and Reporting: Combining CLEM's visualization capabilities with threat intelligence data can provide security teams with a more comprehensive view of their network security posture. This can aid in identifying potential vulnerabilities, prioritizing mitigation efforts, and communicating risks to stakeholders effectively.
In essence, integrating CLEM with threat intelligence platforms can transform it from a standalone behavioral analysis tool into a more powerful and proactive cybersecurity solution capable of detecting, analyzing, and responding to sophisticated cyberattacks more effectively.

Could the reliance on overfitting to specific time windows in CLEM's training process limit its ability to detect novel or rapidly evolving attack strategies?

Yes, CLEM's reliance on overfitting to specific time windows could potentially limit its ability to detect novel or rapidly evolving attack strategies. Here's why:

Overfitting to Known Patterns: By design, overfitting encourages CLEM to learn the specific nuances and patterns present within each time window of training data. While this allows for precise characterization of "normal" behavior within that window, it can make the model less adaptable to deviations from those learned patterns, which might indicate a new attack strategy.

Limited Generalizability: Overfitting can hinder a model's ability to generalize well to unseen data. If an attack employs techniques or behaviors significantly different from those observed in the training windows, CLEM might fail to recognize them as anomalous.

Rapidly Evolving Threats: The cybersecurity landscape is constantly evolving, with attackers continuously developing new techniques and adapting existing ones. CLEM's reliance on historical data for overfitting might not keep pace with these rapid changes, potentially leaving gaps in its detection capabilities.
However, the paper does mention that CLEM is designed to find long-term behavioral changes in streaming network data. This suggests that the time windows used are likely substantial, potentially mitigating some of the limitations of overfitting to very short timeframes.
To address the potential limitations of overfitting, the following strategies could be considered:

Continuous Learning: Implement mechanisms for CLEM to continuously learn and adapt to new data over time. This could involve retraining the model periodically with fresh data or using online learning techniques to update the model's parameters as new data streams in.

Anomaly Scoring with Uncertainty: Instead of relying solely on binary classification (normal vs. anomalous), CLEM could provide anomaly scores with associated uncertainty estimates. This would allow security analysts to prioritize investigation of events with high anomaly scores and high uncertainty, potentially indicating novel threats.

Hybrid Approach: Combine CLEM's behavioral analysis with other detection methods, such as signature-based detection or rule-based systems. This can provide a more comprehensive defense strategy, leveraging the strengths of different approaches to counter the limitations of overfitting.
By incorporating these strategies, CLEM can maintain its ability to characterize normal behavior effectively while also improving its capacity to detect novel and rapidly evolving attack strategies.

What are the ethical implications of using LLMs for cybersecurity, particularly concerning potential biases in the data or the models themselves?

The use of LLMs for cybersecurity, while promising, raises several ethical implications, particularly concerning potential biases:

Biased Datasets: LLMs are trained on massive datasets, which can reflect and amplify existing societal biases. If the training data contains biased information about certain groups, activities, or technologies, the resulting LLM might exhibit discriminatory behavior. For example, if cybersecurity datasets overrepresent attacks originating from specific regions, the LLM might be more likely to flag legitimate traffic from those regions as suspicious.

Discriminatory Outcomes: Biased LLMs can lead to discriminatory outcomes in cybersecurity practices. For instance, a biased model might disproportionately flag individuals from certain demographic groups for additional security checks or unfairly block access to resources based on biased profiling.

Reinforcement of Existing Inequalities: Using biased LLMs in cybersecurity can perpetuate and even exacerbate existing social and economic inequalities. For example, if a biased model leads to increased surveillance or scrutiny of marginalized communities, it can further marginalize those groups and erode trust in digital systems.

Lack of Transparency and Explainability: LLMs are often referred to as "black boxes" due to their complex inner workings, making it challenging to understand the reasoning behind their decisions. This lack of transparency can make it difficult to identify and mitigate biases, potentially leading to unfair or discriminatory outcomes without clear explanations.
To address these ethical implications, it's crucial to:

Ensure Diverse and Representative Datasets: Carefully curate training datasets to ensure they are diverse, representative, and free from biases. This might involve actively seeking out data from underrepresented groups or using techniques to mitigate bias in existing datasets.

Develop Bias Detection and Mitigation Techniques: Invest in research and development of techniques to detect and mitigate biases in LLMs. This could involve developing fairness metrics specific to cybersecurity applications or creating methods for debiasing models during or after training.

Promote Transparency and Explainability: Strive for greater transparency in LLM-based cybersecurity systems. This could involve developing methods to explain the reasoning behind model decisions or creating tools that allow for auditing and monitoring of model behavior.

Establish Ethical Guidelines and Regulations: Develop clear ethical guidelines and regulations for the development and deployment of LLMs in cybersecurity. These guidelines should address issues related to bias, fairness, transparency, and accountability.
By proactively addressing these ethical implications, we can harness the power of LLMs for cybersecurity while ensuring fairness, equity, and accountability in their application.