Abstract

In the rapidly evolving landscape of network security, the need for robust anomaly detection methods has become paramount. This study presents a novel framework that leverages Large Language Models (LLMs) in conjunction with deep learning methodologies to enhance the identification of anomalies in network security systems. The proposed framework integrates LLMs' advanced capabilities to process and analyze textual data associated with network traffic and events, enabling a nuanced understanding of typical versus atypical behaviors in real-time. The research employs a multi-faceted approach, combining quantitative and qualitative techniques to assess the efficacy of the proposed framework. We begin by collecting network traffic data from diverse sources, including intrusion detection systems (IDS), firewall logs, and other pertinent security feeds. The dataset was preprocessed to extract relevant features for analysis. For the model, we developed an ensemble of deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to capture spatial and temporal patterns in the data. The integration of LLMs involves employing techniques such as tokenization and embedding to convert network event logs into a format suitable for deep learning analysis. We also conducted experiments using labeled datasets containing both normal and anomalous behavior. Metrics such as accuracy, precision, recall, and F1-score were utilized to evaluate the model's performance. The results indicate that the proposed framework significantly improves the detection of network anomalies compared to traditional methods. The integration of LLMs enhanced the contextual understanding of network events, leading to better classification and a reduction in false positives. The ensemble of deep learning models achieved an accuracy rate of over 95%, with a notable increase in recall, highlighting the system's ability to identify anomalies that may have gone undetected by conventional methods. Moreover, the use of LLMs allowed for real-time analysis, which is crucial for effective network security management. The study demonstrates that leveraging advanced language models in conjunction with deep learning not only improves detection rates but also facilitates a deeper understanding of the underlying patterns associated with network anomalies.In conclusion, this research contributes a significant advancement in the field of network security, offering a viable solution that harnesses the power of modern AI techniques to combat increasingly sophisticated security threats.