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

TL;DR

This paper discusses the vulnerabilities of deep learning systems to adversarial attacks in cybersecurity, emphasizing the need for early mitigation strategies to prevent real-world consequences like accidents and misclassifications.

Contribution

It highlights the critical security risks of adversarial examples in deep learning and advocates for establishing baseline security standards before deployment.

Findings

Deep learning models are vulnerable to adversarial attacks.

Adversarial attacks can cause critical real-world accidents.

Early mitigation is essential for safe deployment.

Abstract

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. Successful exploit on these systems could lead to critical real world repercussions. For instance, (1) an adversarial attack on a self-driving car running a deep reinforcement learning system yields a direct misclassification on humans causing untoward accidents.(2) a self-driving vehicle misreading a red light signal may cause the car to crash to another car (3) misclassification of a pedestrian lane as an intersection lane that could lead to car crashes. This is just the tip of the iceberg, computer vision deployment are not entirely focused on self-driving cars but on many other areas as well - that would have definitive impact on the real-world. These vulnerabilities must be mitigated at an early stage of development. It is imperative to develop and implement baseline security standards at a global level prior to real-world deployment.