# Real-time malware process detection and automated process killing

## TL;DR

This paper presents a real-time malware detection system that combines machine learning and statistical filtering to identify and automatically kill malicious processes on endpoints, significantly reducing ransomware damage.

## Contribution

It introduces a novel real-time detection and automated response model that acts within seconds, addressing a critical gap in existing malware defense strategies.

## Key findings

- Prevents 92% of ransomware from corrupting files
- Achieves a false positive rate of 14%
- Demonstrates the importance of rapid detection within seconds

## Abstract

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.