# Recent Advancements in Machine Learning For Cybercrime Prediction

arXiv: https://arxiv.org/abs/2304.04819 · HTML: https://mlsanity.com/paper/2304.04819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 98 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [The Curious Case of Machine Learning In Malware Detection](https://mlsanity.com/paper/1905.07573/) (2019)
- [Automated Ransomware Behavior Analysis: Pattern Extraction and Early Detection](https://mlsanity.com/paper/1910.06469/) (2019)
- [Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review](https://mlsanity.com/paper/1502.03552/) (2015)
- [Jasmine: A New Active Learning Approach to Combat Cybercrime](https://mlsanity.com/paper/2108.06238/) (2021)
- [Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails](https://mlsanity.com/paper/2303.00870/) (2023)

## Abstract (arXiv)

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements in cybercrime prediction, highlighting the relevant research. For this purpose, we reviewed more than 150 research articles and discussed 50 most recent and appropriate ones. We start the review with some standard methods cybercriminals use and then focus on the latest machine and deep learning techniques, which detect anomalous behavior and identify potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset. We then focus on active and reinforcement learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. This paper presents a holistic view of cutting-edge developments and publicly available datasets.