**Boosting Cybersecurity with Transfer Learning and Pre-Trai

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Boosting Cybersecurity with Transfer Learning and Pre-Trained AutoEncoders

In the ever-evolving landscape of cybersecurity, machine learning (ML) practitioners face a daunting task: detecting Zero-Day attacks before they wreak havoc. Zero-Day attacks are unpatched vulnerabilities that exploit previously unknown security flaws, making them a significant threat to organizations. One promising approach to address this challenge is leveraging Transfer Learning with pre-trained AutoEncoders.

What are AutoEncoders?

AutoEncoders are a type of neural network that learns to compress and reconstruct input data. They consist of an encoder that maps input data to a lower-dimensional representation (latent space) and a decoder that reconstructs the original input from the latent space. Pre-trained AutoEncoders are networks that have already been trained on a large dataset, allowing them to learn general representations of data.

Why Transfer Learning?

Transfer learning enables ML...

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