On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples
Zolotukhin, M., Miraghaie, P., Zhang, D., & Hämäläinen, T. (2022). On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples. IEEE Access, 10, 126285-126303. https://doi.org/10.1109/access.2022.3225921
Published in
IEEE AccessDate
2022Discipline
Secure Communications Engineering and Signal ProcessingTekniikkaSecure Communications Engineering and Signal ProcessingEngineeringCopyright
© Authors, 2022
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.
...
Publisher
Institute of Electrical and Electronics Engineers (IEEE)ISSN Search the Publication Forum
2169-3536Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/164217034
Metadata
Show full item recordCollections
License
Related items
Showing items with similar title or keywords.
-
On Attacking Future 5G Networks with Adversarial Examples : Survey
Zolotukhin, Mikhail; Zhang, Di; Hämäläinen, Timo; Miraghaei, Parsa (MDPI AG, 2023)The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to ... -
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti (Peregrine Technical Solutions, 2020)Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine ... -
Recent Applications of Explainable AI (XAI) : A Systematic Literature Review
Saarela, Mirka; Podgorelec, Vili (MDPI, 2024)This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over the past three years. ... -
Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
Saarela, Mirka; Geogieva, Lilia (MDPI AG, 2022)Skin cancer is one of the most prevalent of all cancers. Because of its being widespread and externally observable, there is a potential that machine learning models integrated into artificial intelligence systems will ... -
Taxonomy of generative adversarial networks for digital immunity of Industry 4.0 systems
Terziyan, Vagan; Gryshko, Svitlana; Golovianko, Mariia (Elsevier, 2021)Industry 4.0 systems are extensively using artificial intelligence (AI) to enable smartness, automation and flexibility within variety of processes. Due to the importance of the systems, they are potential targets for ...