The paper explores the architecture of Generative Adversarial Networks (GANs) and their use cases in various fields, particularly in cybersecurity. It highlights five key cybersecurity domains where GANs can significantly impact, including Deepfake creation, phishing, and anomaly detection. The study details current and potential GAN techniques that could be exploited for malicious activities. GANs consist of two models: the generator (G), which creates new instances from random noise, and the discriminator (D), which assess the generated sample’s authenticity. While the discriminator gains the ability to discern between authentic fraudulent samples through feedback from the generator, the generator attempts to generate data that closely resembles the original dataset. This adversarial process keeps going until the generator generates samples that are identical to actual data. The generator aims to produce data that mimics the real dataset, while the discriminator learns to distinguish between real and fake samples based on feedback from the generator. This adversarial process continues until the generator produces samples indistinguishable from real data. After training, the generator transforms random input into a compressed representation that aligns with the data distribution of the training set. The model is then capable of generating new examples that mirror the acquired traits of the initial dataset. Both the authentic cases from the dataset and fictitious ones produced by the generator are used to train the discriminator. Once training is complete, the discriminator is no longer needed, as its primary role is to guide the generator during the training phase. A zero sum game is used to describe the relationship between the discriminator and generator, where the generator is penalized for producing detectable fake samples. As the training progresses, the discriminator becomes increasingly confused, indicating that the generator is successfully creating realistic outputs. A perfect equilibrium is not necessary for the generator to be effective, as useful models can still be developed without flawless performance. GANs can be categorized into supervised, unsupervised, and hybrid types, with various models like CGAN, DCGAN, and AAE falling into these categories. Each type has unique characteristics and applications, with some models designed for specific tasks such as image generation or semi-supervised learning. The study underscores the significant potential of GANs in cybersecurity applications, highlighting their ability to create new threats. It suggests that future research should explore the effectiveness of different GAN models for specific tasks to better understand their implications for security. A comprehensive understanding of GAN mechanisms is essential for developing defenses against emerging cyber threats, as outlined in the paper.

Generative Adversarial Networks (GAN) Insights for Cyber Security Applications

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发表日期：

2025年

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Mohammad Shahnawaz Shaikh

摘要

The paper explores the architecture of Generative Adversarial Networks (GANs) and their use cases in various fields, particularly in cybersecurity. It highlights five key cybersecurity domains where GANs can significantly impact, including Deepfake creation, phishing, and anomaly detection. The study details current and potential GAN techniques that could be exploited for malicious activities. GANs consist of two models: the generator (G), which creates new instances from random noise, and the discriminator (D), which assess the generated sample’s authenticity. While the discriminator gains the ability to discern between authentic fraudulent samples through feedback from the generator, the generator attempts to generate data that closely resembles the original dataset. This adversarial process keeps going until the generator generates samples that are identical to actual data. The generator aims to produce data that mimics the real dataset, while the discriminator learns to distinguish between real and fake samples based on feedback from the generator. This adversarial process continues until the generator produces samples indistinguishable from real data. After training, the generator transforms random input into a compressed representation that aligns with the data distribution of the training set. The model is then capable of generating new examples that mirror the acquired traits of the initial dataset. Both the authentic cases from the dataset and fictitious ones produced by the generator are used to train the discriminator. Once training is complete, the discriminator is no longer needed, as its primary role is to guide the generator during the training phase. A zero sum game is used to describe the relationship between the discriminator and generator, where the generator is penalized for producing detectable fake samples. As the training progresses, the discriminator becomes increasingly confused, indicating that the generator is successfully creating realistic outputs. A perfect equilibrium is not necessary for the generator to be effective, as useful models can still be developed without flawless performance. GANs can be categorized into supervised, unsupervised, and hybrid types, with various models like CGAN, DCGAN, and AAE falling into these categories. Each type has unique characteristics and applications, with some models designed for specific tasks such as image generation or semi-supervised learning. The study underscores the significant potential of GANs in cybersecurity applications, highlighting their ability to create new threats. It suggests that future research should explore the effectiveness of different GAN models for specific tasks to better understand their implications for security. A comprehensive understanding of GAN mechanisms is essential for developing defenses against emerging cyber threats, as outlined in the paper.