# PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis

arXiv: https://arxiv.org/abs/1709.06440 · HTML: https://mlsanity.com/paper/1709.06440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Locally Differentially Private Distributed Deep Learning via Knowledge Distillation](https://mlsanity.com/paper/2202.02971/) (2022)

## Abstract (arXiv)

Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this paper, we present PeerHunter, a community behavior analysis based method, which is capable of detecting botnets that communicate via a P2P structure. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Through extensive experiments with real and simulated network traces, PeerHunter can achieve very high detection rate and low false positives.