  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.
