Security risks and countermeasures of adversarial attacks on AI-driven applications in 6G networks: A survey

• Published in: Journal of network and computer applications Vol. 232; p. 104031
• Main Authors: Hoang, Van-Tam, Ergu, Yared Abera, Nguyen, Van-Linh, Chang, Rong-Guey
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: 6G networks; Adversarial anomaly detection; Adversarial attacks; Adversarial defenses; AI-powered 6G applications; Deep neural network; Noise perturbation; O-RAN vulnerabilities; Deep neural network; 6G networks; AI-powered 6G applications; Adversarial defenses; Noise perturbation; O-RAN vulnerabilities; Adversarial attacks; Adversarial anomaly detection
• ISSN: 1084-8045
• DOI: 10.1016/j.jnca.2024.104031

The advent of sixth-generation (6G) networks is expected to start a new era in mobile networks, characterized by unprecedented high demands on dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence (AI) is at the forefront of this progress, optimizing and enabling intelligence for essential 6G functions such as radio resource allocation, slicing, service offloading, and mobility management. However, AI is subject to a wide range of security risks, most notably adversarial attacks. Recent studies, inspired by computer vision and natural language processing, show that adversarial attacks have significantly reduced performance and caused incorrect decisions in wireless communications, jeopardizing the perspective of transforming AI-based 6G core networks. This survey presents a thorough investigation into the landscape of adversarial attacks and defenses in the realm of AI-powered functions within classic wireless networks, open radio access networks (O-RAN), and 6G networks. Two key findings are as follows. First, by leveraging shared wireless networks, attackers can provide noise perturbation or signal sampling for interference, resulting in misclassification in AI-based channel estimation and signal classification. From these basic weaknesses, 6G introduces new threat vectors from AI-based core functionalities, such as malicious agents in federated learning-based service offloading and adversarial attacks on O-RAN near-real-time RIC (xApp). Second, adversarial training, trustworthy mmWave/Terahertz datasets, adversarial anomaly detection, and quantum technologies for adversarial defenses are the most promising strategies for mitigating the negative effects of the attacks. This survey also identifies possible future research topics for adversarial attacks and countermeasures in 6G AI-enabled technologies. •Security risks and countermeasures of adversarial attacks in essential 6G functions such as radio resource allocation and mobility management•Security threats of AI-related core functionalities in open radio access networks (O-RAN)•Adversarial defense, trustworthy AI, and quantum technologies for robust 6G applications•Future research directions on adversarial attacks and defense in AI-driven 6G networks