Machine Learning and Large Language Models-based Techniques for Cyber Threat Detection: A Comparative Study

This study presents a comparative analysis of Machine Learning (ML) and Large Language Models (LLMs) for Cyber Threat Detection. We evaluate the performance of various ML algorithms (e.g. Random Forest, Gradient Boosting) and fine-tuned LLM algorithms (e.g. LlaMA3, Falcon) on multiple datasets, cons...

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Bibliographic Details
Main Authors: Abdennebi, Anes, Morsli, Reda, Kara, Nadjia, Ould-Slimane, Hakima
Format: Conference Proceeding
Language:English
Subjects:
Computational efficiency
Computational modeling
Cyber Threat Detection
Cybersecurity
Hazards
Internet of Things
Large language models
Machine Learning
Machine learning algorithms
Measurement
Random forests
Scalability
Threat assessment
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Summary:This study presents a comparative analysis of Machine Learning (ML) and Large Language Models (LLMs) for Cyber Threat Detection. We evaluate the performance of various ML algorithms (e.g. Random Forest, Gradient Boosting) and fine-tuned LLM algorithms (e.g. LlaMA3, Falcon) on multiple datasets, considering metrics such as F1-score, real-world applicability, explainability, interpretability, scalability, and adaptability to evolving threats. Our results show that while ML models often have strong performance and interpretability, LLMs show the potential for high accuracy, especially when dealing with complex hazard patterns. However, the computational requirements and ambiguities associated with LLMs present challenges to widespread adoption. To maximize the benefits of both approaches, we propose several future research directions leveraging both techniques. Future research should focus on improving the interpretability of LLM, reducing the computational cost, and building a synergistic solution harnessing ML models and LLMs.
ISSN:2159-6972
DOI:10.1109/CIoT63799.2024.10756998