Mechanism to Detect the Suspicious Activity in the Network using Random Forest Algorithm

Abstract

Due to new technology, cyberattacks and network-related assaults have dramatically grown. The Distributed Denial of Service (DDoS) attack, in which the hacker uses several dispersed resources against the targeted system, is one of the main risks in these attacks. As DDoS traffic looks just like regular traffic, it is difficult to identify DDoS attacks. We employ the machine learning technology known as the Random Forest Tree to identify the DDoS assault and categorize regular traffic from abnormal traffic. In this work, the dataset including all the properties of the incoming traffic is used to retrieve the incoming traffic. To create an appropriate model, the dataset is trained using the Random Forest technique. Each time the incoming traffic is given into this model as its input, it is then utilized to distinguish between the regular traffic and aberrant traffic.