Abstract:

Distributed Denial of Service (DDoS) attacks have become increasingly prevalent in recent years, posing significant threats to the availability, reliability, and security of online services. These attacks overwhelm targeted systems with a massive volume of traffic, rendering them inaccessible to legitimate users. Traditional defense mechanisms, such as firewalls and rule-based intrusion detection systems, are often insufficient in detecting and mitigating modern, sophisticated DDoS attacks in real-time. In response to this challenge, this project proposes a machine learning-based classification and prediction framework for the early detection and prevention of DDoS attacks. The proposed system leverages supervised machine learning algorithms to analyze network traffic and classify it as either normal or malicious. Key features such as packet size, flow duration, protocol type, and traffic volume are extracted and used to train various models including Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. These models are evaluated based on accuracy, precision, recall, and F1-score to identify the most effective classifier for real-time DDoS detection. In addition to classification, predictive models are developed to forecast potential attack patterns using historical traffic data. This predictive capability enables proactive defense strategies by alerting systems before an attack fully materializes. Experimental results demonstrate that the proposed machine learning models achieve high accuracy in identifying and predicting DDoS attacks, thereby significantly enhancing the resilience of network infrastructure against cyber threats.

Description:

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