  Perimeter-based detection is no longer sufficient for mitigating the threat
posed by malicious software. This is evident as antivirus (AV) products are
replaced by endpoint detection and response (EDR) products, the latter allowing
visibility into live machine activity rather than relying on the AV to filter
out malicious artefacts. This paper argues that detecting malware in real-time
on an endpoint necessitates an automated response due to the rapid and
destructive nature of some malware.
  The proposed model uses statistical filtering on top of a machine learning
dynamic behavioural malware detection model in order to detect individual
malicious processes on the fly and kill those which are deemed malicious. In an
experiment to measure the tangible impact of this system, we find that
fast-acting ransomware is prevented from corrupting 92% of files with a false
positive rate of 14%. Whilst the false-positive rate currently remains too high
to adopt this approach as-is, these initial results demonstrate the need for a
detection model which is able to act within seconds of the malware execution
beginning; a timescale that has not been addressed by previous work.
