An Intelligent Deep Feature Based Intrusion Detection System for Network Applications

The network's digital applications and functions are vulnerable to get attacks from malicious events. Hence, an Intrusion Detection System (IDS) is the required process for the network application to protect the information from unauthenticated malicious events. Many IDS have been implemented on the basis of neural modules for predicting the unauthenticated access that is present in the network medium. But, there are several difficulties on specifying the malicious features from network users. To address this problem, the present research article has planned to develop a novel Krill herd-based Deep Belief Intrusion Forecasting with suitable parameters to detect the present malicious features based on user behaviors. Initially, the data was pre-processed and entered into the classification layer. Consequently, feature extraction and attack specification has been performed. Moreover, the planned model is executed in the python environment, and the scalability score has been measured using dual datasets that are NSL-KDD and CICIDS. Here, incorporating the krill function has helped earn the desired outcomes. Also, attacks like DoS, probe, R2L, and probe have been included in the trained NSL-KDD and CICIDS. Finally, the designed model has earned a better outcome than the compared model by achieving high accuracy and a lower error rate.