Design of Detection System using Deep Learning Algorithm for Attack on Network

System administrators can use a Network Intrusion Detection System (NIDS) to identify network security breaches in their organisations. Creating a flexible and effective NIDS to protect against unexpected and unanticipated attacks, however, presents numerous obstacles. f-measure values, precision, recall, and precision. For the development of such an efficient and versatile NIDS, We recommend a strategy based on deep learning. With NSL-KDD, a benchmarked dataset regarding penetration of network, Self-taught Learning (STL) is implemented by us, a deep learning-dependent methods. We show the results of our technique and compare them to those of a few previous investigations. The measures studied include accuracy, precision, recall, and f-measure scores.