# Joint Detection of Malicious Domains and Infected Clients

arXiv: https://arxiv.org/abs/1906.09084 · HTML: https://mlsanity.com/paper/1906.09084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

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

- [Learning Explainable Representations of Malware Behavior](https://mlsanity.com/paper/2106.12328/) (2021)

## Abstract (arXiv)

Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains.