Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward

TL;DR

This paper examines the vulnerability of future cognitive self-organizing networks to adversarial attacks, highlighting significant threats and proposing directions for enhancing their robustness using machine learning techniques.

Contribution

It provides an analysis of adversarial attack impacts on CSON and suggests future research directions to improve network resilience.

Findings

Adversarial attacks pose significant threats to CSON.

Current machine learning techniques in CSON are vulnerable.

Future work should focus on developing robust defenses.

Abstract

Future communications and data networks are expected to be largely cognitive self-organizing networks (CSON). Such networks will have the essential property of cognitive self-organization, which can be achieved using machine learning techniques (e.g., deep learning). Despite the potential of these techniques, these techniques in their current form are vulnerable to adversarial attacks that can cause cascaded damages with detrimental consequences for the whole network. In this paper, we explore the effect of adversarial attacks on CSON. Our experiments highlight the level of threat that CSON have to deal with in order to meet the challenges of next-generation networks and point out promising directions for future work.