mlsanityData to 14 Oct 2026

A Review of Topological Data Analysis for Cybersecurity

arXiv 2202.08037 ↗First posted 16 Feb 2022cs.CR cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

In cybersecurity it is often the case that malicious or anomalous activity can only be detected by combining many weak indicators of compromise, any one of which may not raise suspicion when taken alone. The path that such indicators take can also be critical. This makes the problem of analysing cybersecurity data particularly well suited to Topological Data Analysis (TDA), a field that studies the high level structure of data using techniques from algebraic topology, both for exploratory analysis and as part of a machine learning workflow. By introducing TDA and reviewing the work done on its application to cybersecurity, we hope to highlight to researchers a promising new area with strong potential to improve cybersecurity data science.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 47 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

17 of 47 references.

References · 17

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5 more
  1. 2019BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchaincited by 12
  2. 2019Topological Machine Learning with Persistence Indicator Functionscited by 11
  3. 2019Harnessing the power of Topological Data Analysis to detect change points in time seriescited by 5
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2022Topological Data Analysis for Anomaly Detection in Host-Based Logscited by 1
  2. 2024Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Managementcited by 0
  3. 2026From Classical to Topological Neural Networks Under Uncertaintycited by 0