Unsupervised Models with LOF and PCA for Robust DDOS Attack Detection

Abstract

The need for robust and efficient Distributed Denial of Service (DDoS) attack detection methods has become increasingly evident in today's networked world. Existing approaches, while commendable, often exhibit limitations that hinder their effectiveness. This paper introduces an innovative approach that leverages Local Outlier Factor (LOF) in conjunction with Principal Component Analysis (PCA) for unsupervised DDoS attack detection. Existing methods often fall short in addressing the evolving nature of DDoS attacks, struggling to maintain precision, accuracy, and recall rates. Our proposed model addresses these limitations by harnessing the power of LOF and PCA, offering a more adaptive and dynamic detection framework. LOF enables the identification of outliers in network traffic patterns, while PCA reduces the dimensionality of the data, enhancing the model's efficiency and speed. The advantages of our approach are manifold. It not only achieves superior performance in terms of precision, accuracy, recall, speed, and Area Under the ROC Curve (AUC) when compared to existing methods but also ensures a higher level of adaptability in detecting emerging DDoS attack vectors & sets. This adaptability is critical in today's ever-evolving threat landscape. The impact of this work extends beyond the realm of academia. In practical terms, our model offers network administrators a potent tool for safeguarding their infrastructures against DDoS attacks. By improving detection rates and reducing false positives, it contributes to the overall security posture of networks, ensuring uninterrupted services and enhanced user experience levels.