A comparison of several intrusion detection methods using the NSL-KDD dataset

Abstract

The increasing significance of cybersecurity underscores the critical necessity of addressing evolving methods of hackers. This research investigates the way to classify and predict cyber-attacks on the NSL-KDD dataset using intrusion detection methods the investigation contrasts the capabilities of various algorithms, including RNN, MLP, CNN-LSTM, and ANN, in recognizing attacks. The results indicate that both MLP and RNN have the greatest efficiency and effectiveness for different time frames. these findings demonstrate the necessity of Constant evaluation and enhancement of intrusion detection systems in order to remain aware of the dynamic nature of the cyber threat landscape. Addressing cybersecurity issues necessitates a comprehensive approach that combines computational enhancements, human talent, organizational policies, and regulatory frameworks in order to create a powerful and stable cybersecurity system.