The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity
Open accessAbstract
Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.
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Cited by
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
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Machine learning and data analytics for the IoT
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Self-citationFederated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
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Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning
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Metadata
- Authors
- Zeadally, S., Adi, E., Baig, Z., & Khan, I. A.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 23817-23837
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3001275383
- Cited by
- 316
- References
- 15
- FWCI
- 15.68
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zeadally, S., Adi, E., Baig, Z., & Khan, I. A. (2020). Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity. IEEE Access. https://doi.org/10.1109/access.2020.2968045
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
