The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.
Application of deep learning to cybersecurity: A survey
Abstract
Abstract Cutting edge Deep Learning (DL) techniques have been widely applied to areas like image processing and speech recognition so far. Likewise, some DL work has been done in the area of cybersecurity. In this survey, we focus on recent DL approaches that have been proposed in the area of cybersecurity, namely intrusion detection, malware detection, phishing/spam detection, and website defacement detection. First, preliminary definitions of popular DL models and algorithms are described. Then, a general DL framework for cybersecurity applications is proposed and explained based on the four major modules it consists of. Afterward, related papers are summarized and analyzed with regard to the focus area, methodology, model applicability, and feature granularity. Finally, concluding remarks and future work are discussed including the possible research topics that can be taken into consideration to enhance various cybersecurity applications using DL models.
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Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access · doi:10.1109/access.2022.3204051 · Cited by 450
Machine Learning in IoT Security: Current Solutions and Future Challenges
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986444 · Cited by 129
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art
Imteaj, A., Mamun Ahmed, K., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2023) · Adaptation, Learning, and Optimization · doi:10.1007/978-3-031-11748-0_2 · Cited by 86
A comprehensive survey and taxonomy on privacy-preserving deep learning
Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21
Metadata
- Authors
- Mahdavifar, S., & Ghorbani, A. A.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 347
- Pages
- 149-176
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2921573932
- Cited by
- 325
- References
- 154
- FWCI
- 24.65
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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