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A Survey of Large Language Models in Cybersecurity (2402.16968v1)

Published 26 Feb 2024 in cs.CR and cs.AI

Abstract: LLMs have quickly risen to prominence due to their ability to perform at or close to the state-of-the-art in a variety of fields while handling natural language. An important field of research is the application of such models at the cybersecurity context. This survey aims to identify where in the field of cybersecurity LLMs have already been applied, the ways in which they are being used and their limitations in the field. Finally, suggestions are made on how to improve such limitations and what can be expected from these systems once these limitations are overcome.

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Citations (3)
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Summary

  • The paper introduces a novel Mixture-of-Experts framework that leverages specialized LLMs for targeted cybersecurity tasks.
  • It surveys existing LLM applications in intrusion detection, vulnerability assessment, and penetration testing while addressing challenges like context loss and high false positives.
  • The framework employs an intelligent gating model to route tasks efficiently, promising improved precision, scalability, and adaptability in combating cyber threats.

Exploring the Integration of LLMs in Cybersecurity: A Mixture-of-Experts Approach

Introduction to the Study's Aims and Methodology

The increasing reliance on digital infrastructure across various sectors underscores the critical need for robust cybersecurity measures. Traditional defense mechanisms, while effective to an extent, often struggle to keep pace with the sophistication of contemporary cyber threats. In response to this challenge, the advent of LLMs has opened new frontiers in AI, offering promising prospects for enhancing cybersecurity efforts. This paper presents a comprehensive survey aimed at exploring the application of LLMs within the field of cybersecurity, particularly focusing on vulnerability assessment and penetration testing tasks. By surveying existing implementations and proposing a novel Mixture-of-Experts framework, the paper seeks to harness the capabilities of specialized LLMs to address the complex landscape of cyber threats.

The Current State of LLMs in Cybersecurity

Previous research has demonstrated the efficacy of deep neural networks in various cybersecurity applications, including malware detection, network intrusion prevention, and password guessing. However, issues such as the lack of model explainability and high false positive rates have marred their widespread acceptance. LLMs, with their advanced neural network architectures, have shown immense potential in generalizing across different tasks, presenting a new avenue for cybersecurity applications. The survey within this paper reveals an increasing interest in incorporating LLMs for cybersecurity, yet it also highlights a noticeable gap between the rapid advancements in LLM technology and their application within the cybersecurity domain.

Identifying Issues and Challenges

While LLMs exhibit remarkable text generation capabilities, their performance tends to diminish in complex, evolving tasks due to issues like loss of context and hallucinations. These challenges are particularly pronounced in cybersecurity applications, where accuracy and reliability are paramount. Traditional methods to mitigate these limitations, such as fine-tuning, in-context learning, and retrieval-augmented generation, although helpful, do not fully address the complexities involved in cybersecurity tasks.

Proposing a Novel Solution: The Mixture-of-Experts Framework

To overcome the aforementioned challenges, the paper proposes a Mixture-of-Experts (MoE) framework that leverages the specialization of different foundation LLMs for various cybersecurity subtasks. This approach aims to harness the collective intelligence of these models, essentially creating a system where multiple "experts" in specific domains collaboratively contribute to a comprehensive cybersecurity solution. The proposed framework envisions a gating model that intelligently routes tasks to the most suitable expert model, ensuring a targeted, efficient response to various cybersecurity challenges.

Implications and Future Research Directions

The integration of LLMs in cybersecurity, as proposed in the Mixture-of-Experts framework, holds significant potential to revolutionize cybersecurity practices. By enhancing the precision, scalability, and adaptability of cybersecurity mechanisms, this approach offers a promising solution to the ever-evolving threat landscape. However, realizing this potential necessitates further research to refine the expertise of specialized LLMs, expand the framework's coverage to encompass a broader range of cybersecurity domains, and empirically validate its effectiveness in real-world scenarios. Future exploration should also address ethical considerations surrounding AI in cybersecurity, ensuring that these advanced systems are developed and deployed responsibly.

Conclusion

The paper underscores the burgeoning potential of LLMs to enhance cybersecurity measures through a novel Mixture-of-Experts framework. By addressing current challenges and outlining a path for future research, this paper contributes to the evolving dialogue on the integration of AI in cybersecurity. As digital threats grow in complexity, the collaborative intelligence model offered by the proposed framework represents a forward-thinking solution, promising a new era of intelligent cybersecurity defenses.

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