AI and 6G Security: Opportunities and Challenges

Siriwardhana, Yushan; Porambage, Pawani; Liyanage, Madhusanka; Ylianttila, Mika

doi:10.1109/eucnc/6gsummit51104.2021.9482503

Cited by 144 publications

(76 citation statements)

References 31 publications

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“…The progress in cybersecurity research and innovative countermeasures will limit their occurrence probability. However, in case of success, their impact and the damage they will cause will be devastating, and orders of magnitude larger than their current level of damage (Siriwardhana et al, 2021).…”

Section: Malware and Attacksmentioning

confidence: 99%

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Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

Yaacoub¹

2022

Preprint

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<p>This paper investigates the synergy between 6G and AI. It argues that they can unlock future horizons, by discussing how they can address future challenges in healthcare, transportation, virtual reality, education, resource management, robotics, in addition to public safety and warfare. However, these great opportunities come also with greater risk. Therefore, the paper provides an overview of the security risks and challenges, along with possible mitigation techniques. <br></p>

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Section: Malware and Attacksmentioning

confidence: 99%

“…In fact, although AI techniques can be used to detect and mitigate security vulnerabilities and intrusions, advances in AI can also be used for perfecting the attacks targeting the systems described above. Thus, it can be a double-edged sword used for both attack and defense (Siriwardhana et al, 2021;Buchholz et al, 2022).…”

Section: Risksmentioning

confidence: 99%

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

Yaacoub¹

2022

Preprint

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“…The authors of [22] investigate the role of AI in IoT security for possible cyber attacks, emphasizing the model poisoning attack, i.e., where a machine learning model's training data are poisoned. The study [32] provides a comprehensive review of the opportunities and challenges in AI-based security and private provision as well as proposes solutions for 6G and beyond networks.…”

Section: Purpose and Contributionsmentioning

confidence: 99%

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

Kuzlu¹,

Çatak²,

Cali³

et al. 2022

Preprint

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The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting the beamforming prediction using deep learning algorithms in these networks. This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks (DNNs) in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem. It is indicated that the initial DNN model is vulnerable against adversarial attacks, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Momentum Iterative Method (MIM), because the initial DNN model is sensitive to the perturbations of the adversarial samples of the training data. This study also offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence (AI)-based models used in the millimeter-wave (mmWave) beamforming prediction. Furthermore, the proposed scheme can be used in situations where the data are corrupted due to the adversarial examples in the training data. Experimental results show that the proposed methods effectively defend

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“…Their advantages are higher accuracy and diversity. Primarily, advanced ML algorithms and DL models can deal with different cybersecurity problems [81]: poisonous attacks by tampering training data with malicious samples, evasion attacks by injecting disorders and outliers to testing data to circumvent the learned model, API-based attacks to pilfer prediction outcomes, infrastructure physical attacks and communication tampering by interfering communication-computing connections and shutting AI systems down [84]. However, most existing ML-powered security mechanisms are unable to explain their final decisions and response actions.…”

Section: B Trust and Securitymentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

Self Cite

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When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

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AI and 6G Security: Opportunities and Challenges

Cited by 144 publications

References 31 publications

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

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