eXtreme gradient boosting

In networks characterized by imbalanced traffic, detecting malicious cyber-attacks poses a significant challenge due to their ability to blend seamlessly with regular data volumes.  This creates a formidable hurdle for Network Intrusion Detection Systems (NIDS) striving for accurate and timely identification.  The imbalance in normal and attack data, coupled with the diversity among attack categories, complicates intrusion detection.  This research proposes a novel approach to address this issue by combining Extreme Gradient Boosting with variational autoencoder (XIDINT

The field of time series forecasting has grown significantly over the past several years and is now highly active.  In numerous application domains, deep neural networks are exact and powerful.  They are among the most popular machine learning techniques for resolving big data issues because of these factors.  Historically, there have been numerous methods for accurately predicting the subsequent change in time series data.  The time series forecasting problem and its mathematical underpinnings are first articulated in this study.  Following that, a description of the m