International Journal for Innovative Engineering and Management Research

This paper introduces a new approach for detecting Distributed Denial of Service (DDoS) attacks using an entropy-based method.DDoS attacks are a major threat to network security, as they overwhelm target systems with a massive volume of malicious traffic, disrupting normal operations and causing service outages.Traditional detection methods, such as signature-based techniques or anomaly detection, often struggle to identify new or unknown attack types, especially zero-day attacks.In contrast, our method uses entropy measures to assess the randomness and unpredictability of network traffic, enabling us to spot abnormal patterns that suggest a DDoS attack.By calculating entropy for different network traffic characteristics-like packet size, inter-arrival time, and protocol distribution-our approach can effectively detect deviations from normal traffic, which may signal a DDoS attack.To improve detection accuracy and efficiency, we also incorporate machine learning techniques that learn from historical data to distinguish between normal and attack traffic.Through extensive testing on real-world network datasets, we show that our entropy-based method is not only effective at detecting DDoS attacks but also minimizes false positives.This approach offers a promising solution for enhancing network security by enabling early detection and timely response to DDoS threats, helping to protect critical network infrastructure and services.