IMPROVING DETECTION FOR INTRUSION USING DEEP LSTM WITH HYBRID FEATURE SELECTION METHOD

Abstract

Due to the importance of the intrusion detection system, which is considered supportive of enhancing network security. Therefore, we seek to increase the efficiency of intrusion detection systems through the use of deep learning mechanisms. However, intrusion detection algorithms still suffer from problems in the process of classification and determining the presence and type of attack, which causes a decrease in the detection rate, an increase in the number of false alarms, and reduces system performance. This is due to a large number of redundant features that are not relevant to the dataset. To find the solve for this problem, here propose a hybrid algorithm based on the use of the feature selection technique, which helps in reaching the goal optimally by choosing the best and most important features. And it works by integrating three ways to minimize the features by deleting the static features, that do not have much value from the information gain and is done before the training stage by the deep learning model of LSTM as preprocessing for the CSE-CIC-IDS data set, which helps in improving the performance of the system By minimizing the time for processing and increasing the detection rate and accuracy ratio. The results of the experiment showed a high accuracy of 99.84%.