DDOS Attack Classifier Using Machine Learning

DDOS Attack Classifier Using Machine Learning

Aryan Chauhan1, Prince Gandhi2, Nisarg Patel3 , Dhara Parikh4

123Department of Information Technology, Institute of Information Technology, Krishna School Of Emerging Technology & Applied Research, KPGU University , Varnama, Vadodara, Gujarat, India

4  Assistant Professor, Department of Information Technology and Engineering, Krishna School Of Emerging

Technology & Applied Research, KPGU University. Varnama, Vadodara, Gujarat, India

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - The increasing frequency and complexity of Distributed Denial of Service (DDoS) attacks present substantial challenges to network security. Traditional Intrusion Detection Systems (IDS) often struggle to detect intricate attack patterns in real-time. This research introduces a machine learning-based classifier designed to accurately identify and classify DDoS attacks. Leveraging the Intrusion Detection Evaluation Dataset (CIC-IDS2017), which contains realistic and labeled network traffic, we assess multiple models—Random Forest, Logistic Regression, Gradient Boosting, and Naive Bayes. By incorporating advanced feature selection and hyperparameter tuning, this classifier effectively minimizes false positives and demonstrates strong performance across metrics like accuracy, precision, and recall, making it a promising candidate for real-time DDoS detection in modern networks.

Key Words:  Distributed Denial of Service (DDoS), Machine Learning,  Intrusion Detection System (IDS), Random Forest, Logistic Regression, Naive Bayes, Gradient Boosting,  Exploratory Data Analysis (EDA)