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

arXiv: https://arxiv.org/abs/1810.07242 · HTML: https://mlsanity.com/paper/1810.07242/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2018.

## Built on by (most cited first, 6 of 6)

- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems](https://mlsanity.com/paper/2105.10707/) (2021)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses](https://mlsanity.com/paper/2210.13289/) (2022)
- [Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models](https://mlsanity.com/paper/2303.02669/) (2023)
- [Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification](https://mlsanity.com/paper/2310.06855/) (2023)

## Abstract (arXiv)

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.