During this time, COVID-19 has affected the lifestyles of many individuals; in the meantime, an enormous amount of users are connected with the internet. This will also increase the chance of network intrusion due to congestion and overloading of the server. So, to cope with this problem, the authors proposed an automated intrusion detection system (IDS) which helps in monitoring the traffic and service request. The model is used to identify the illegal access and counterparts with static checking capabilities of the firewall. The classical KDDCup 99 dataset is used for training and testing purposes.
Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms
