Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques
| dc.contributor.author | Elluri, Lavanya | |
| dc.contributor.author | Mandalapu, Varun | |
| dc.contributor.author | Vyas, Piyush | |
| dc.contributor.author | Roy, Nirmalya | |
| dc.date.accessioned | 2023-05-15T19:54:31Z | |
| dc.date.available | 2023-05-15T19:54:31Z | |
| dc.date.issued | 2023-04-10 | |
| dc.description.abstract | Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using increasingly sophisticated techniques to breach security systems and steal sensitive data. In recent years, machine learning, deep learning, and transfer learning techniques have emerged as promising tools for predicting cybercrime and preventing it before it occurs. This paper aims to provide a comprehensive survey of the latest advancements in cybercrime prediction using above mentioned techniques, highlighting the latest research related to each approach. For this purpose, we reviewed more than 150 research articles and discussed around 50 most recent and relevant research articles. We start the review by discussing some common methods used by cyber criminals and then focus on the latest machine learning techniques and deep learning techniques, such as recurrent and convolutional neural networks, which were effective in detecting anomalous behavior and identifying potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset, and 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. Overall, this paper presents a holistic view of cutting-edge developments in cybercrime prediction, shedding light on the strengths and limitations of each method and equipping researchers and practitioners with essential insights, publicly available datasets, and resources necessary to develop efficient cybercrime prediction systems. | en_US |
| dc.description.uri | https://arxiv.org/abs/2304.04819 | en_US |
| dc.format.extent | 27 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2fo8k-itaf | |
| dc.identifier.uri | https://doi.org/10.48550/arXiv.2304.04819 | |
| dc.identifier.uri | http://hdl.handle.net/11603/27913 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | en_US |
| dc.rights | Attribution 4.0 International (CC BY 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.title | Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques | en_US |
| dc.type | Text | en_US |