Artificial Neural Network Technique for DDOS Attack Prediction in Cyber-IOT System

An intrusion-detection IoT framework is designed to analyze unauthorized access or security breaches in IoT networks. These systems typically use a combination of hardware and software to analyze network traffic, identify anomalies, and alert security personnel when a potential threat is detected. An intrusion-detection system (IDS) is a one type of security checking model that may be used to monitor a set of connections or devices in order to check for telltale indicators of possible infiltration. During the course of the last several years, there has been a general rise in the number as well as the intensity of cyber attacks. As a consequence of this, there is a need for study of security in cyber world and the prevention of assaults, such as the implementation of intrusion-detection. Businesses that rely on the internet absolutely have to make protecting their data online one of their top responsibilities. Within the scope of this study, this paper proposes a neural network technique as a means of anticipating assaults on IDS. Python Spyder is the software that make use of for this simulation.