DEVELOPMENT OF A MULTI-MODAL AI ALGORITHM FOR PROACTIVE AUTHENTICA-TION THREAT DETECTION IN 6G NETWORKS

Cargbo Daniel Bartolomeo
University of Sierra Leone, Freetown, Sierra Leone;
Moscow Technical University of Communications and Informatics, Moscow, Russia
danielsondaniels25@gmail.com

V. B. Kreyndelin
Moscow Technical University of Communications and Informatics, Moscow, Russia
Institute of Radio and Information Systems (IRIS), Vienna, Austria

DOI: 10.36724/2664-066X-2026-12-1-41-49

SYNCHROINFO JOURNAL. Volume 12, Number 1 (2026). P. 41-49.

Abstract

This research paper presents a comprehensive design and evaluation of a multi-modal artificial intelligence (AI) algorithm aimed at achieving proactive authentication threat detection in sixth-generation (6G) networks. The evolution of 6G networks introduces high data throughput, extremely low latency, and ubiquitous connectivity, creating complex security challenges. To mitigate these, the proposed algorithm leverages multiple modalities biometric, behavioral, contextual, and network data to construct an adaptive, self-learning authentication framework. The algorithm integrates deep neural networks (DNNs), graph-based modeling, and reinforcement learning (RL) to dynamically detect potential threats before breaches occur. Comprehensive simulations conducted in a virtual 6G environment demonstrate superior detection accuracy (98.2%) and reduced false-positive rates compared to existing methods. The results suggest that multi-modal AI represents a viable approach for predictive and intelligent security in 6G environment.

Keywords:  6G networks, authentication, multi-modal AI, proactive security, intrusion detection, deep learning

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