Next-Generation Network Intrusion Detection: Leveraging Deep Learning Techniques


  • G. Tarun Datta, A. Sasi Vadana, A. Venkata Akhil, K. Mythily Sai Chandana, Venkata Vara Prasad Padyala


Security of computer networks, Intrusion Detection Systems, advanced machine learning, streamlined data encoding, specific network intrusion dataset.


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.


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How to Cite

G. Tarun Datta. (2024). Next-Generation Network Intrusion Detection: Leveraging Deep Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2727–2733. Retrieved from



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