Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain

Understand

Deep learning models are known to solve classification and regression problems by employing a number of epoch and training samples on a large dataset with optimal accuracy.

• However, that doesn't mean they are attack-proof or unexposed to vulnerabilities.
• Newly deployed systems particularly on a public environment (i.e public networks) are vulnerable to attacks from various entities.
• Moreover, published research on deep learning systems (Goodfellow et al., 2014) have determined a significant number of attacks points and a wide array of attack surface that has evidence of exploitation from adversarial examples.