Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

Hackers have evolved at a faster rate in terms of their capabilities because of precise technological developments like networks and communication systems.These crooks are constantly looking for new ways to jeopardize the security of computer networks (CN).Intrusion detection systems (IDS) consequently automatically become important parts of a CN.An IDS is a piece of software or hardware that tracks a company's network for potential threats.Meanwhile, an IDS is capable of reacting to and reporting any fraudulent activity.Node-based or host-based IDS (HIDS) [1, 2] Network based IDS (NIDS) or distributed-based IDS (DIDS), and hybrid-based IDS (HYIDS) are the three basic categories into which IDSs fall based on the unique IDS operation concept, this classification was created.