A REVIEW OF GENERATIVE ADVERSARIAL NETWORKS FOR SECURITY APPLICATIONS
Swarajya Madhuri Rayavarapu
madhurirayavarapu.rs@andhrauniversity.edu.inAndhra University, Department of Electronics and Communication Engineering (India)
https://orcid.org/0009-0007-7559-2142
Shanmukha Prasanthi Tammineni
Andhra University, Department of Electronics and Communication Engineering (India)
https://orcid.org/0009-0000-5352-2265
Sasibhushana Rao Gottapu
Andhra University, Department of Electronics and Communication Engineering (India)
Aruna Singam
Andhra University, Department of Electronics and Communication Engineering (India)
Abstract
Advances in cybersecurity are crucial for a country's economic and national security. As data transmission and storage exponentially increase, new threat detection and mitigation techniques are urgently needed. Cybersecurity has become an absolute necessity, with the ever-increasing transmitted networks from day to day causing exponential growth of data that is being stored on servers. In order to thwart sophisticated attacks in the future, it will be necessary to regularly update threat detection and data preservation techniques. Generative adversarial networks (GANs) are a class of unsupervised machine learning models that can generate synthetic data. GANs are gaining importance in AI-based cybersecurity systems for applications such as intrusion detection, steganography, cryptography, and anomaly detection. This paper provides a comprehensive review of research on applying GANs for cybersecurity, including an analysis of popular cybersecurity datasets and GAN model architectures used in these studies.
Keywords:
generative models, cyber security, machine learning, neural networks, unsupervised learningReferences
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Authors
Swarajya Madhuri Rayavarapumadhurirayavarapu.rs@andhrauniversity.edu.in
Andhra University, Department of Electronics and Communication Engineering India
https://orcid.org/0009-0007-7559-2142
Authors
Shanmukha Prasanthi TammineniAndhra University, Department of Electronics and Communication Engineering India
https://orcid.org/0009-0000-5352-2265
Authors
Sasibhushana Rao GottapuAndhra University, Department of Electronics and Communication Engineering India
Authors
Aruna SingamAndhra University, Department of Electronics and Communication Engineering India
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