Enhanced Network Security: Machine Learning-Based DDOS Detection

Abstract

The exponential growth of internet users has seriously jeopardised the safety of online assets. Ensuring safety is of utmost importance as internet usage continues to grow exponentially. Making claims on denial-of-service attacks. It is trying to come up with a cutting-edge cyber-security plan in response to this dynamic danger. In this paper, we present a machine learning framework that can identify DDoS attacks by combining logistic regression, K-nearest neighbor, and random forest. To test the proposed models, we use the latest NSL KDD dataset. Results from our test further demonstrate how effectively the suggested model differentiates DDoS attacks. In comparison to the best attack detection approaches currently available, our findings show that our recommended model is superior. Enterprises, cloud services, internet service providers (ISPs), e-commerce, healthcare, government, telecoms, gaming, the Internet of Things (IoT), education, and media and entertainment are just a few of the many sectors that can benefit from improved network security through machine learning-based DDoS detection.