MRFM: A timely detection method for DDoS attacks in IoT with multidimensional reconstruction and function mapping

Abstract

To address the slow response time of existing detection modules to the Internet of Things (IoT) Distributed Denial of Service (DDoS) attacks, along with their low feature differentiation and poor detection performance, we propose MRFM, a timely detection method with multidimensional reconstruction and function mapping. Firstly, we employ a queue mechanism to capture and store incoming network traffic data within a predefined time frame. Subsequently, we introduce a multidimensional reconstruction neural network model, specifically designed to reconstruct quantitative features based on their respective indices by adjusting the loss function. This process is followed by the computation of multidimensional reconstruction errors and the transformation of vectors into mapping features, thereby augmenting the disparities among various types of traffic data and promoting the similarity within the same category of traffic data. Lastly, we extract frequency information from the qualitative feature matrix using information entropy calculations, enriching the feature profile of individual traffic instances. The experimental results on two benchmark datasets show that MRFM can effectively detect different types of DDoS attacks. Notably, MRFM consistently outperforms other existing methods, exhibiting an average metric improvement of up to 9.61 %.