With the rise of next-generation wireless systems, Deep Learning (DL)-based security solutions are gaining widespread adoption. One key area DL impacts is beamforming prediction in mmWave communication. This study investigates how vulnerable AI-based models are to adversarial attacks, which can disrupt network performa...
Thulitha Senevirathna, Bartlomiej Siniarski, Madhusanka Liyanage et al.· IEEE Transactions on Machine...· 0 citations
Artificial Intelligence-driven Radio Access Networks (AI-RAN) are a core paradigm of 6G, but the tight coupling of AI models, open interfaces, and distributed control loops raises security and trust challenges that cryptographically relevant quantum computers worsen by breaking the public-key cryptography underpinning...
Pasindu Udugahapattuwa, Chamitha de Alwis, Engin Zeydan et al.· 2026 International Conferenc...· 0 citations
Generative Artificial Intelligence (GenAI) offers transformative potential for cybersecurity, enabling automation, adaptability, and intelligence against increasingly sophisticated threats. This paper examines the development, training, and deployment of GenAI models for threat detection, anomaly analysis, incident res...
C. Moremada, Engin Zeydan, Madhusanka Liyanage· 2026 International Conferenc...· 0 citations
A unified perspective on decentralized intelligence for 6G is developed, arguing that decentralization, trustworthiness, explainability, and sustainability must be designed jointly rather than treated as independent requirements.
Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán et al.· 0 citations
The emergence of sixth-generation (6 G) networks demands a radical shift in how telecommunication infrastructures are designed, managed, and operated. This paper presents a comprehensive vision for an evolvable 6 G telco cloud and service provisioning platform that supports multi-provider, multi-technology environments...
Engin Zeydan, Pasika Sashmal Ranaweera, Madhusanka Liyanage· 2026 IEEE International Conf...· 0 citations
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