Network Intrusion Detection Using Optimal Perception with Cuckoo Algorithm

To safeguard computer networks from intruders, intrusion detection systems have been created. These systems operate in conjunction with firewalls and other security measures to guarantee the safety and efficiency of the computer system. An intrusion detection system is a tool designed to detect and pinpoint attacks and vulnerabilities within a network or computer system. It subsequently notifies the system administrator of them. The primary challenge with intrusion detection systems is enhancing their speed and precision in detecting intruders. This article explores a novel technique for identifying attempts to infiltrate computer systems. The system utilizes a hybrid approach involving the cuckoo algorithm and perceptron neural network. This novel approach can detect intrusion data more accurately than previous methods and enhance the detection rate by over 1%. The system utilizes the cuckoo method to choose a subset of characteristics, which are then analyzed based on the frequency of various attribute types in intrusive and normal data using an optimum perceptron. The system has been evaluated and the implementation has yielded a detection accuracy of 89.8%, representing a substantial enhancement compared to earlier approaches.