Examining Machine Learning for 5G and Beyond Through an Adversarial Lens

Usama, Muhammad; Ilahi, Inaam; Qadir, Junaid; Mitra, Rupendra Nath; Marina, Mahesh K.

doi:10.1109/mic.2021.3049190

Cited by 25 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [268], the authors present a cautionary view on using ML in 5G/6G by highlighting the adversarial dimension spanning multiple types of ML (SL, UL, or RL) and support this through several case studies. They also examine various approaches to mitigate such adversarial ML attacks by evaluating ML models' robustness and calling attention to ML-oriented research issues in 5G/6G.…”

Section: End-to-end Aspectsmentioning

confidence: 86%

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since mobile networks are becoming progressively more heterogeneous and more complex. In this context, a promising approach is the adoption of novel network automation solutions and, in particular, of zero-touch management techniques. In this work, we refer to zero-touch management as a fully autonomous network management solution with human oversight. This survey sits at the crossroad between zero-touch management and mobile and wireless network research, effectively bridging a gap in terms of literature review between the two domains. In this paper, we first provide a taxonomy of network management solutions. We then discuss the relevant state-of-the-art on autonomous mobile networks. The concept of zero-touch management and the associated standardization efforts are then introduced. The survey continues with a review of the most important technological enablers for zero-touch management. The network automation solutions from the RAN to the core network, including end-toend aspects such as security, are then surveyed. Finally, we close this article with the current challenges and research directions.

show abstract

Section: End-to-end Aspectsmentioning

confidence: 86%

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…Attacks against AI/ML-based modulation recognition models is probably the most well-studied category of the attacks against wireless communication systems based on adversarial example generation [74]- [77]. In a real world scenario, the adversary would have access to neither the exact input of the receiver nor the modulation type selected by the target model.…”

Section: B Adversarial Examples In 5gmentioning

confidence: 99%

“…To attack intelligent channel decoding frameworks, the adversary can try to generate a perturbation that causes decoding errors at the receiver [74], [78]. In white-box settings, this perturbation can be carried out e.g.…”

Section: B Adversarial Examples In 5gmentioning

confidence: 99%

On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples

Zolotukhin

Miraghaie

Zhang³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and allocation of the necessary resources to their customers in a dynamic, robust and trustworthy way. Dependability of the future generation networks on accurate and timely performance of its artificial intelligence components means that disturbance in the functionality of these components may have negative impact on the entire network. As a result, there is an increasing concern about the vulnerability of intelligent machine learning driven frameworks to adversarial effects. In this study, we evaluate various adversarial example generation attacks against multiple artificial intelligence and machine learning models which can potentially be deployed in future 5G networks. First, we describe multiple use cases for which attacks on machine learning components are conceivable including the models employed and the data used for their training. After that, attack algorithms, their implementations and adjustments to the target models are summarised. Finally, the attacks implemented for the aforementioned use cases are evaluated based on deterioration of the objective functions optimised by the target models.

show abstract

“…In terms of adversarial attacks, adversarial examples may refer to fake data instances that are carefully crafted by adversaries. For example, Usama et al [30] utilized crafted noise to fool the modulation classifier. In addition to some intuitive changes, DNN can also be used to generate fake data for attack.…”

Section: Adversarial Attacksmentioning

confidence: 99%

Intelligent networking in adversarial environment: challenges and opportunities

Zhao

et al. 2022

Sci. China Inf. Sci.

View full text Add to dashboard Cite

Although deep learning technologies have been widely exploited in many fields, they are vulnerable to adversarial attacks by adding small perturbations to legitimate inputs to fool targeted models. However, few studies have focused on intelligent networking in such an adversarial environment, which can pose serious security threats. In fact, while challenging intelligent networking, adversarial environments also bring about opportunities. In this paper, we, for the first time, simultaneously analyze the challenges and opportunities that the adversarial environment brings to intelligent networking. Specifically, we focus on challenges that the adversarial environment will pose on the existing intelligent networking. Furthermore, we investigate frameworks and approaches that combine adversarial machine learning with intelligent networking to solve the existing deficiencies of intelligent networking. Finally, we summarize the issues, including opportunities and challenges, which can allow researchers to focus on intelligent networking in adversarial environments.

show abstract

Examining Machine Learning for 5G and Beyond Through an Adversarial Lens

Cited by 25 publications

References 11 publications

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples

Intelligent networking in adversarial environment: challenges and opportunities

Contact Info

Product

Resources

About