Generative adversarial networks in cyber security: Literature review

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Literature review evaluates GAN applications in cybersecurity and anomaly detection, highlighting ethical implications.

Generative adversarial networks significantly improve detection systems accuracy by 25%, enhancing cybersecurity.
The systematic review examines GAN applications in intrusion detection for cybersecurity and anomaly detection in medical diagnostics.
Assessment metrics like Fréchet Inception Distance are utilized to evaluate synthetic data quality in GANs.
Ethical concerns related to deepfakes emphasize the need for regulatory frameworks in the use of generative adversarial networks.

Arafat et al. (2025) studied this question.

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View Full Paper

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Authors

Literature review evaluates GAN applications in cybersecurity and anomaly detection, highlighting ethical implications.

Generative adversarial networks significantly improve detection systems accuracy by 25%, enhancing cybersecurity.
The systematic review examines GAN applications in intrusion detection for cybersecurity and anomaly detection in medical diagnostics.
Assessment metrics like Fréchet Inception Distance are utilized to evaluate synthetic data quality in GANs.
Ethical concerns related to deepfakes emphasize the need for regulatory frameworks in the use of generative adversarial networks.

Arafat et al. (2025) studied this question.

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