ARP Spoofing Attack Detection to Prevent DDoS Attack

In today's digital landscape, cyber threats such as Distributed Denial of Service (DDoS) Cyberattacks pose a serious threat to organizations, often resulting in substantial financial losses and harm to their reputation. These attacks can disrupt the availability of resources, preventing legitimate users from accessing essential services.to mitigate such threats, we propose a machine learning-based approach utilizing Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) algorithms. The primary objective is to monitor and analyze network activity in real time, identifying potential attack patterns. This research focuses on detecting ARP spoofing attacks, which are often used to execute Distributed Denial-of-Service (DDoS) attacks. We propose a real-time detection mechanism that efficiently identifies and mitigates such threats. The approach is tested in different network environments, and results show that it significantly reduces unauthorized access and improves overall security. Our findings emphasize the need for robust security measures to prevent network traffic manipulation and unauthorized intrusions.