Λεπτομέρειες βιβλιογραφικής εγγραφής
| 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] |
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| Βάση Δεδομένων: |
Complementary Index |