Exploring Botnet Attacks with Rapid Miner: A Comprehensive Study

The fast growth of networks has been shown to have a clear causal relationship with the recent uptick in botnetbased attacks. The increasing use of botnets in various types of cyberattacks, such as denial of service (DoS), distributed denial of service (DDoS), backdoor attacks, and other types of cyberattacks, presents a significant challenge to the network security of organizations. By using the collected bandwidth and system resources of its victims, the botnet has the potential to cause damage that cannot be repaired to the network. The purpose of this paper is to conduct a comprehensive analysis of botnet attacks, with a concentration on DoS and DDoS attacks. It is easy to draw presumptions and construct views on bots based on their activities and traits. Models were trained via machine learning, specifically Auto Modeling, to help defend themselves against attacks launched by botnets. The trained model provides both tabular and visual representations of the outcomes of DoS and DDoS attacks. The model represents the results of DoS and DDoS attacks in tabular and graphical form. Random forest and generalized linear models were used because they showed the best performance on the dataset. Wireshark was used to ensure that the opening of the monitored network went off without a hitch. The pcap data extracted from the network was loaded in Rapid Miner for model training.This paper analyzes the potential for DoS and DDoS attacks.