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NAVAL
POSTGRADUATE
SCHOOL
MONTEREY, CALIFORNIATHESIS
USING GENERATIVE ADVERSARIAL NETWORKS FOR
INTRUSION DETECTION IN CYBERPHYSICAL SYSTEMS
by
Jessica L. Purser
September 2020
Thesis Advisor:
Advisor:They
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How to fill out using generative adversarial networks
How to fill out using generative adversarial networks
01
Gather a dataset containing examples of the desired output.
02
Design and train a generator network to generate samples similar to the dataset.
03
Design and train a discriminator network to distinguish between real and generated samples.
04
Train the generator and discriminator networks in a competitive setting to improve the quality of generated samples.
05
Evaluate the performance of the trained GAN model and fine-tune if necessary.
Who needs using generative adversarial networks?
01
Researchers in the fields of computer vision, natural language processing, and image generation.
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Data scientists and machine learning engineers interested in generating realistic data samples.
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Companies looking to enhance their data augmentation techniques or generate synthetic data for training machine learning models.
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What is using generative adversarial networks?
Generative adversarial networks are a type of artificial intelligence algorithm that is used for generating new data samples.
How to fill out using generative adversarial networks?
Users can fill out generative adversarial networks by training a generator and a discriminator neural network to work against each other to generate new data samples.
What is the purpose of using generative adversarial networks?
The purpose of using generative adversarial networks is to generate synthetic data samples that closely resemble the original dataset, which can be used for various machine learning tasks.
What information must be reported on using generative adversarial networks?
Users must report the training process, the architecture of the generator and discriminator networks, and any modifications made to improve the quality of data generation.
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