Building Trustworthy 6G Networks with Generative Adversarial Learning
The imminent dawn of sixth-generation (6G) networks promises a future of unparalleled connectivity and communication speeds. However, this technological leap necessitates robust security measures to counter increasingly sophisticated cyberthreats targeting the intricate 6G infrastructure. This chapter investigates the potential of Generative Adversarial Learning (GALs) as a transformative tool for building trustworthy 6G networks. With the advent of 6G networks on the horizon, ensuring trustworthiness in communication systems becomes paramount. This chapter proposes a novel approach leveraging GAL to fortify the security and reliability of 6G networks. In traditional network security paradigms, adversaries exploit vulnerabilities, necessitating constant reactive measures. However, the proactive nature of GANs enables the creation of realistic synthetic data to train robust Intrusion Detection Systems (IDS). By simulating diverse attack scenarios, a GAN-based IDS can identify and adapt to emerging threats, mitigating potential risks in real time. Moreover, GANs facilitate the generation of synthetic network traffic, enabling thorough testing of network defenses without risking actual data. Taking a proactive stance enables network operators to predict and preempt potential vulnerabilities before they are exploited. Our solution involves harnessing the power of Generative Adversarial Networks (GANs) to address 6G network security comprehensively. GANs create authentic network traffic, allowing IDS to be trained effectively in identifying and mitigating actual cyberthreats. Moreover, GANs can learn to discern typical network patterns, thus alerting to potential anomalies that may signify ongoing or imminent attacks. This proactive strategy empowers security teams to maintain an edge in navigating the constantly evolving cyberthreat landscape. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors.
Blessing, N.R. Wilfred; Alankritha, S.; Kanna, R. Rajesh; Varghese, Jithu; Doshi, Ruchi
Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks;pp.48-55
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https://doi.org/10.1201/9781003652991-4 https://www.scopus.com/pages/publications/105040302824?origin=resultslist
Blessing N.R.W., College of Computing and Information Sciences, University of Technology and Applied Sciences-Ibri, Oman; Alankritha S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Kanna R.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Varghese J., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Doshi R., Department of Computer Science & IT, Universidad Azteca, Chalco, Mexico