Optimization of Intrusion Detection with Deep Learning: A Study Based on the KDD Cup 99 Database

With the exponential increase in cyberattacks, the need for effective and scalable network intrusion detection systems (IDS) is critical.This study evaluates the effectiveness of applying a deep neural network model designed for network attack classification using the KDD Cup 99 database.Our approach involves meticulous data preparation and model training optimization, which leads to notable improvements in the accuracy of detecting various types of attacks.The results highlight the potential of deep learning techniques to significantly enhance IDS performance.This study provides valuable insights into the practical application of deep learning in network security and suggests avenues for future research aimed at improving IDS capabilities and adapting to emerging cyber threats.