Training LSTM Model for Data Classification and Prediction

Abstract

After looking at the available literature, most studies have found that further newlineinvestigation is required to enhance the accuracy of IDS. As is also highlighted in the newlinepresent literature, a variety of variables influence the total amount of time spent on newlinetraining activities. Traditional studies have only suggested a few of viable IDS newlinestrategies. If the results and recommendations of this study are implemented, the newlinemethod used to forecast IDS with any degree of accuracy would be substantially newlinechanged. Taking the training model into consideration, the work described here newlineshould lead to a scalable and flexible IDS detection approach. It is expected that newlinetraining the proposed model on a big dataset would increase its prediction power. newlineSuccess in IDS detection requires that similar methods be used in future research. The newlinefinding has huge implications for improving IDS predictionS. newline