Title: AI-based intrusion detection and adaptive access control for enhancing security in 6G network slicing

Authors: R. Kanthavel; R. Dhaya

Addresses: School of Electrical and Communication Engineering, Papua New Guinea University of Technology, Lae-411, Papua New Guinea ' School of Electrical and Communication Engineering, Papua New Guinea University of Technology, Lae-411, Papua New Guinea

Abstract: As 6G networks become a reality, they will certainly usher in new security challenges that will need to be addressed, particularly due to network slicing in multi-tenant environments. In this work, we proposed an AI-based framework consisting of: (1) a deep learning-based intrusion detection system (AI-IDS), and (2) a reinforcement learning-based adaptive access control system (AACS) for slice-level security. The proposed system identifies threats (known and unknown), dynamically develops, and enforces access policies based on normal user behavior. The framework was evaluated using various benchmark datasets in a simulated 6G slicing environment. Overall, the proposed AI framework achieved ~93% detection accuracy, low false positive rates (~4%), and reasonably rapid response times (~75 ms). Results showed the proposed framework provided higher adaptability and accuracy over traditional fixed security mechanisms.

Keywords: 6G network slicing; AI-based intrusion detection; AACS; adaptive access control system; RL; reinforcement learning; DL; deep learning; cybersecurity in multi-tenant networks.

DOI: 10.1504/IJCNDS.2026.153762

International Journal of Communication Networks and Distributed Systems, 2026 Vol.32 No.3, pp.286 - 316

Received: 25 May 2025
Accepted: 06 Jul 2025
Published online: 26 May 2026 *

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