Classification and Prediction of Distributed Denial of Service attacks using Random Forest and a comparative analysis of Machine Learning algorithms

Classification and Prediction of Distributed Denial of Service Attacks using Random Forest and a Comparative Analysis of Machine Learning Algorithms

Sandhiya M1, Ramyasri G2, Tharun surya M3

S. Kavishree, M.Tech, Assistant Professor

1Department of Information Technology, Sri Venkateswara College of Engineering

2Department of Information Technology, Sri Venkateswara College of Engineering

3Department of Information Technology, Sri Venkateswara College of Engineering

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Abstract - Hackers or cybercriminals utilize DDoS (Distributed Denial of Service) attacks, a type of malicious cyber-attack, to render an online service, network resource, or host system inaccessible to its intended users on the Internet. The proposed system forecasts DDoS attacks using random forest, and we also compared four different classification techniques. The Random Forest, Logistic Regression, Support Vector Classifier, K-Nearest Neighbors, and Decision Tree algorithms are the machine learning models on which this project is based. Python was utilized as a simulator and the DDoS dataset was used to achieve the proposed goal. In order to identify the model performance after applying the machine learning methods, we built a confusion matrix.

Key Words:  Random Forest, DDoS, Machine Learning, Classification, attacks.