# Ransomware Detection using Process Memory

arXiv: https://arxiv.org/abs/2203.16871 · HTML: https://mlsanity.com/paper/2203.16871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System](https://mlsanity.com/paper/1710.04843/) (2017)
- [Malware Classification Using Long Short-Term Memory Models](https://mlsanity.com/paper/2103.02746/) (2021)
- [Ransomware Analysis using Feature Engineering and Deep Neural Networks](https://mlsanity.com/paper/1910.00286/) (2019)

## Abstract (arXiv)

Ransomware attacks have increased significantly in recent years, causing great destruction and damage to critical systems and business operations. Attackers are unfailingly finding innovative ways to bypass detection mechanisms, whichencouraged the adoption of artificial intelligence. However, most research summarizes the general features of AI and induces many false positives, as the behavior of ransomware constantly differs to bypass detection. Focusing on the key indicating features of ransomware becomes vital as this guides the investigator to the inner workings and main function of ransomware itself. By utilizing access privileges in process memory, the main function of the ransomware can be detected more easily and accurately. Furthermore, new signatures and fingerprints of ransomware families can be identified to classify novel ransomware attacks correctly. The current research used the process memory access privileges of the different memory regions of the behavior of an executable to quickly determine its intent before serious harm can occur. To achieve this aim, several well-known machine learning algorithms were explored with an accuracy range of 81.38 to 96.28 percents. The study thus confirms the feasibility of utilizing process memory as a detection mechanism for ransomware.