• Suresh Kumar Rulaniya
• Gourav Mitawa

In modern life, networks play a significant role, and cybersecurity has been a core field of study. A cyber-security-important intrusion detection system (IDS) monitors the network’s state of software & hardware. Despite decades of growth, there are still challenges for current IDSs to boost detector accuracy, reduce false alarm rates, and detect unknown attacks. Many researchers have been working to create IDSs that force machine learning (ML) methods to solve the above problems. The critical variations between normal data or abnormal data can be found through machine learning procedures with high precision. Deep learning (DL) is an area that has remarkable performance and has become a center of research. In this research, we have used an efficient Neural Network method as the taxonomy of the IDS attacks. The results are remarkable with Recurrent Neural Network (RNN) on the NSL-KDD dataset using python as the simulation tool.

Network Security; Intrusion Detection System Machine Learning; RNN; NSL-KDD

"An IDS For Attacks Classification Using Recurrent Neural Network For Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.483-486, June-2019, Available :http://www.jetir.org/papers/JETIR1908C62.pdf

"An IDS For Attacks Classification Using Recurrent Neural Network For Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp483-486, June-2019, Available at : http://www.jetir.org/papers/JETIR1908C62.pdf