Next-Generation Network Intrusion Detection: Leveraging Deep Learning Techniques - G. Tarun Datta (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The Network Intrusion Detection System (NIDS) plays a pivotal role as an indispensable tool for system administrators in the discernment and detection of network security breaches within their respective organizations. However, the advancement of a multifaceted and exceedingly proficient Network Intrusion Detection System (NIDS) is not devoid of its proportionate set of obstacles, particularly when confronted with unanticipated and capricious cyber assaults. We propose a ground-breaking and cutting-edge deep learning paradigm as the underpinning for the creation of an efficient and adaptable Network Intrusion Detection System (NIDS).The present study utilizes the Self Learning (STL) methodology, a sophisticated DL technique, for the purpose of analyzing the NSL-KDD dataset. This particular dataset is widely recognized as a benchmark in the field of network intrusion analyzing. In this study, we present the performance evaluation of the proposed methodology and conduct a comparative analysis with a range of previous research endeavors. The metrics being considered encompass a range of quantitative measures, including accuracy, precision, recall, and f-measure values.