Supervised machine learning is often used to detect phishing websites. However, the scarcity of phishing data for training purposes limits the classifier's performance. Further, machine learning ...

As AI becomes more central to enterprise security, ignoring adversarial risks is like leaving the back door wide open.

Architectural Diversity: Autoencoder-based approaches span a wide range—from basic feedforward variants to more complex designs including convolutional VAEs, adversarial AEs, and blind denoising ...

According to the Gartner Market Guide for Adversarial Exposure Validation dated March 2025, AEV is defined "as technologies that deliver consistent, continuous and automated evidence of the ...

That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

Keywords: protein system, conformational space, variational autoencoder, molecular dynamics, deep learning Citation: Tian H, Jiang X, Trozzi F, Xiao S, Larson EC and Tao P (2021) Explore Protein ...

In this article, we propose a novel technique network for unsupervised unmixing which is based on the adversarial AE, termed as adversarial autoencoder network (AAENet), to address the above problems.

Supervised Adversarial Autoencoder Our model for conditional generation is based on a Supervised Adversarial Autoencoder (Supervised AAE, SAAE) (Makhzani et al., 2015) shown in Figure 1. The ...

Deep generative models are attracting great attention as a new promising approach for molecular design. A variety of models reported so far are based on either a variational autoencoder (VAE) or a ...