Authors: Mayuri Dongre, Tanmay Lanjewar, Vedant Chaple
Abstract: With the rapid expansion of internet-based applications, cloud services, and digital communication platforms, cybersecurity threats have become increasingly complex and harmful. Among these threats, Distributed Denial of Service (DDoS) attacks are considered one of the most disruptive network-based attacks because they overwhelm targeted servers or networks with excessive traffic, causing downtime, service interruption, and financial loss. Traditional security mechanisms such as firewalls and rule-based intrusion detection systems often fail to detect evolving DDoS attack patterns in their early stages. This research focuses on applying supervised machine learning techniques for early DDoS attack detection by analyzing network traffic behavior and classifying malicious activities. The proposed system performs data preprocessing, feature extraction, traffic analysis, model training, and attack classification using supervised learning algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighbors (KNN). The study aims to improve detection accuracy, reduce false alarms, and strengthen real-time cybersecurity monitoring. Results indicate that supervised learning models provide reliable performance in identifying suspicious traffic patterns and can significantly enhance proactive defense mechanisms in network infrastructures.
