The application of a large language model for reducing false positives in anomaly detection tasks in network traffic

Every year, network threats become more sophisticated and complex, which requires researchers in the field of network security to seek and develop new and more advanced methods for detecting security threats. Despite the fact that constant research is being conducted in this area and researchers are improving machine learning algorithms, false positive triggers of intrusion detection systems remain a significant problem. In this regard, the development of methods and approaches to reduce the number of false positive positives is one of the most urgent tasks.