Detection and Prevention of Malicious Activities in Vulnerable Network Security Using Deep Learning

Abstract

In today’s environment, application protection is most critical because of the progression and sharing of data and knowledge techniques that generate new value-added services across various potential attacks. As a consequence, they have established numerous internet platforms. A Network Intrusion Detection System (NIDS) allows administrators within network processes to identify network security breaches. That being said, when a robust and reliable NIDS is built for unpredictable and unpredicted attacks, several issues arise. It is verified by the test conducted that the convolutional neural network is successful for NIDS. In this work, a deep-learning methodology to integrate quite a robust and scalable different cloud detection system has been highlighted. The device uses a recurrent neural network (RNN) monitored by a training algorithm to detect visible and invisible assaults. Initially, the information is preprocessed for input to both the neural network utilizing Data Balancing and standardization. To construct a learning model by preprocessing, the RNN technique was implemented to the refined data. The entire KDD Cup 99 was used to validate that. The false alarm rate, accuracy and detection rate have been measured to assess the detection accuracy of the RNN model when all is said and done. In comparison, we test and compare various algorithms for deep learning, i.e. Cloud environment RNN, CNN, DNN and PNN algorithm for network intrusion detection.