GPU based Real-time Super Hosts Detection at Distributed Edge Routers

TL;DR

This paper introduces CBAA, a GPU-accelerated algorithm for real-time detection of super hosts at edge routers, enabling fast identification in high-speed networks for various security and resource management applications.

Contribution

The paper presents a novel, parallel algorithm CBAA that efficiently detects super hosts in real time using GPU acceleration at edge routers.

Findings

CBAA accurately detects super hosts in real-time.

CBAA outperforms existing methods in speed and accuracy.

Experimental results on real-world networks validate its effectiveness.

Abstract

The super host is a special host on the network which contacts with many other hosts during a certain time window. They play important roles in network researches such as scanners detection, resource allocation, spam filtering and so on. How to find super hosts in real time is the foundation of these applications. In this paper, a novel algorithm, denoted as CBAA, is proposed to solve this problem at edge routers. CBAA divides network traffic into different parts. A cube of bits array is devised to store hosts' linking information of different traffic parts when scanning packets. At the end of each time window, CBAA restores super hosts very fast because there are only a fraction of super hosts in each traffic part. CBAA is also a parallel algorithm. It's easy to deploy CBAA in GPU to deal with high-speed network traffic in real time. Experiments on a real-world core network prove the advantage of our algorithm.