# Hopper: Modeling and Detecting Lateral Movement (Extended Report)

arXiv: https://arxiv.org/abs/2105.13442 · HTML: https://mlsanity.com/paper/2105.13442/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2021.

## Built on by (most cited first, 2 of 2)

- [KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data](https://mlsanity.com/paper/2410.08390/) (2024)
- [Designing a reliable lateral movement detector using a graph foundation model](https://mlsanity.com/paper/2504.13527/) (2025)

## Abstract (arXiv)

In successful enterprise attacks, adversaries often need to gain access to additional machines beyond their initial point of compromise, a set of internal movements known as lateral movement. We present Hopper, a system for detecting lateral movement based on commonly available enterprise logs. Hopper constructs a graph of login activity among internal machines and then identifies suspicious sequences of loginsthat correspond to lateral movement. To understand the larger context of each login, Hopper employs an inference algorithm to identify the broader path(s) of movement that each login belongs to and the causal user responsible for performing a path's logins. Hopper then leverages this path inference algorithm, in conjunction with a set of detection rules and a new anomaly scoring algorithm, to surface the login paths most likely to reflect lateral movement. On a 15-month enterprise dataset consisting of over 780 million internal logins, Hop-per achieves a 94.5% detection rate across over 300 realistic attack scenarios, including one red team attack, while generating an average of <9 alerts per day. In contrast, to detect the same number of attacks, prior state-of-the-art systems would need to generate nearly 8x as many false positives.