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.

翻译：网络犯罪对全球组织和个人构成日益严重的威胁，犯罪分子利用复杂技术突破安全系统并窃取敏感数据。本文旨在全面综述网络犯罪预测领域的最新进展，重点介绍相关研究成果。为此，我们审阅了超过150篇研究论文，并讨论了其中50篇最新且最具代表性的文献。本文首先概述网络犯罪分子常用的标准方法，继而聚焦于用于检测异常行为与识别潜在威胁的最新机器学习和深度学习技术。我们还探讨了迁移学习——即允许在某一数据集上训练的模型适用于另一数据集的方法。随后，我们关注作为网络犯罪预测早期算法研究组成部分的主动学习与强化学习。最后，我们讨论了网络犯罪预测中的关键创新、研究空白及未来研究机遇。本文提供了前沿发展与公开数据集的全面视角。