Abstract

Now a days Use of internet is rapidly increasing in our daily digital life. In April 2024, there were 5.44 billion internet users globally Cyber-attacks have become one of the biggest problems of the world. There are many traditional network security tools and techniques for Detection of intrusion in Network with security tools like below. Anti-viruses’ Anti-malwares, Encryption-decryption, Access control. Firewalls. And intrusion detection system (IDS) approaches currently aren't able to handle Detect and categorized Different Types of Fake cyberattacks on computer networks and cannot control Network flow. The proposed Hybrid deep neural network (DNN) and GAN (Generative Adversarial Network) Exhaustive Feature Selection (EFS) With Input as KDD_CUP 99 Dataset can Identify and categorized Different Fake Attack Types such as R2L, U2R, Probe, DoS, & Normal.