ResidualSketch: Enhancing Layer Efficiency and Error Reduction in Hierarchical Heavy Hitter Detection with ResNet Innovations

TL;DR

ResidualSketch is a new algorithm that improves hierarchical heavy hitter detection by reducing errors and memory use through ResNet-inspired residual connections, leading to more accurate and faster network traffic analysis.

Contribution

It introduces ResidualSketch, a novel hierarchical heavy hitter detection method that leverages residual blocks to enhance accuracy and efficiency in network traffic analysis.

Findings

Outperforms existing methods in accuracy

Reduces memory overhead significantly

Maintains low update latency

Abstract

In network management, swiftly and accurately identifying traffic anomalies, including Distributed Denial-of-Service (DDoS) attacks and unexpected network disruptions, is essential for network stability and security. Key to this process is the detection of Hierarchical Heavy Hitters (HHH), which significantly aids in the management of high-speed IP traffic. This study introduces ResidualSketch, a novel algorithm for HHH detection in hierarchical traffic analysis. ResidualSketch distinguishes itself by incorporating Residual Blocks and Residual Connections at crucial layers within the IP hierarchy, thus mitigating the Gradual Error Diffusion (GED) phenomenon in previous methods and reducing memory overhead while maintaining low update latency. Through comprehensive experiments on various datasets, we demonstrate that ResidualSketch outperforms existing state-of-the-art solutions in terms of accuracy and update speed across multiple layers of the network hierarchy. All related codes of ResidualSketch are open-source at GitHub.