A MACHINE LEARNING BASED CLASSIFICATION AND PREDICTION TECHNIQUE FOR DDOS ATTACKS

Abstract

Distributed Denial of Service (DDoS) attacks represent one of the most significant cybersecurity threats to modern networks and online services. These attacks aim to overwhelm target systems, servers, or network resources with massive volumes of malicious traffic, resulting in service disruption, performance degradation, and financial losses. Traditional security mechanisms often struggle to detect and mitigate sophisticated DDoS attacks due to their evolving patterns and increasing complexity. This paper presents a machine learning-based classification and prediction technique for DDoS attacks that leverages intelligent data analysis to identify malicious traffic and forecast potential attack occurrences. The proposed framework utilizes network traffic features, packet statistics, and behavioral characteristics to train machine learning models capable of distinguishing between normal and attack traffic. Various classification algorithms are employed to improve detection accuracy, while predictive models analyze traffic trends to anticipate future attack events. Experimental analysis demonstrates that the proposed approach effectively detects DDoS attacks with high accuracy, reduces false alarm rates, and enhances network security. The integration of classification and prediction capabilities provides a proactive defense mechanism for protecting critical network infrastructures against large-scale cyber threats.