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Unveiling network intrusions: Leveraging large language models for anomaly detection in cybersecurity.

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Τίτλος: Unveiling network intrusions: Leveraging large language models for anomaly detection in cybersecurity.
Συγγραφείς: Patel, Vraj, Savani, Bhargavi, Shah, Bela, Thakkar, Amit
Πηγή: AIP Conference Proceedings; 2025, Vol. 3255 Issue 1, p1-5, 5p
Θεματικοί όροι: Language models, Pattern recognition systems, Anomaly detection (Computer security), Computer network security, Internet security, Intrusion detection systems (Computer security)
Περίληψη: Large Language Models (LLMs) are recognized for their flexibility which has benefited a variety of domains. One such area of study is log analysis for cybersecurity where LLMs can be a great asset. This research delves deep into utilization of Large Language Models (LLMs), namely RoBERTa and MegatronBERT, to analyze network vulnerabilities, with a specific focus on Hadoop Distributed File System (HDFS) logs. Through training these Large Language Models (LLMs) on this dataset, the work aims to compare the accuracy of identifying outliers. The approach includes preprocessing the data, extracting relevant features, and the utilization of progressive LLM architectures for pattern recognition. By performing a number of experiments, the study demonstrates the effectiveness of LLMs in enhancing network security, offering a promising avenue for proactive defense strategies in modern IT environments. [ABSTRACT FROM AUTHOR]
Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+security%22">Computer network security</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink>
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  Data: Large Language Models (LLMs) are recognized for their flexibility which has benefited a variety of domains. One such area of study is log analysis for cybersecurity where LLMs can be a great asset. This research delves deep into utilization of Large Language Models (LLMs), namely RoBERTa and MegatronBERT, to analyze network vulnerabilities, with a specific focus on Hadoop Distributed File System (HDFS) logs. Through training these Large Language Models (LLMs) on this dataset, the work aims to compare the accuracy of identifying outliers. The approach includes preprocessing the data, extracting relevant features, and the utilization of progressive LLM architectures for pattern recognition. By performing a number of experiments, the study demonstrates the effectiveness of LLMs in enhancing network security, offering a promising avenue for proactive defense strategies in modern IT environments. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1063/5.0254175
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        Text: English
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      – SubjectFull: Language models
        Type: general
      – SubjectFull: Pattern recognition systems
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      – SubjectFull: Anomaly detection (Computer security)
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      – SubjectFull: Computer network security
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      – SubjectFull: Internet security
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      – SubjectFull: Intrusion detection systems (Computer security)
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              M: 01
              Text: 2025
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