# Safeguard: Security Controls at the Software Defined Network Layer

arXiv: https://arxiv.org/abs/2601.17355 · HTML: https://mlsanity.com/paper/2601.17355/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2026.

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

- [ModTrans: Translating Real-world Models for Distributed Training Simulator](https://mlsanity.com/paper/2604.01607/) (2026)

## Abstract (arXiv)

Improvements in software defined networking allow for policy to be informed and modified by data-driven applications that can adjust policy to accommodate fluctuating requirements at line speed. However, there is some concern that over-correction can occur and cause unintended consequences depending on the data received. This is particularly problematic for network security features, such as machine-learning intrusion detection systems. We present Safeguard, a rule-based policy that overlaps a data-driven policy to prevent unintended responses for edge cases in network traffic. We develop a reference implementation of a network traffic classifier that enforces firewall rules for malicious traffic, and show how additional rulesets to allow known-good traffic are essential in utilizing a data-driven network policy.