# Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks arXiv: https://arxiv.org/abs/2502.20627 ยท HTML: https://mlsanity.com/paper/2502.20627/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 143 references that are in the map. - First posted on arXiv 28 Feb 2025. ## Builds on (most cited first, 30 of 33) - [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019) - [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018) - [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016) - [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020) - [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019) - [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015) - [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020) - [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020) - [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018) - [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021) - [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021) - [ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks](https://mlsanity.com/paper/1812.01124/) (2018) - [IoT Network Security from the Perspective of Adversarial Deep Learning](https://mlsanity.com/paper/1906.00076/) (2019) - [AMF: Aggregated Mondrian Forests for Online Learning](https://mlsanity.com/paper/1906.10529/) (2019) - [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021) - [A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends](https://mlsanity.com/paper/1505.07919/) (2015) - [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022) - [Security and privacy for 6G: A survey on prospective technologies and challenges](https://mlsanity.com/paper/2108.11861/) (2021) - [Concept Drift Detection in Federated Networked Systems](https://mlsanity.com/paper/2109.06088/) (2021) - [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021) - [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024) - [A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems](https://mlsanity.com/paper/2210.01985/) (2022) - [LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles](https://mlsanity.com/paper/2208.03399/) (2022) - [Learning-Aided Physical Layer Authentication as an Intelligent Process](https://mlsanity.com/paper/1808.02456/) (2018) - [AI for Beyond 5G Networks: A Cyber-Security Defense or Offense Enabler?](https://mlsanity.com/paper/2201.02730/) (2022) - [From Design to Deployment of Zero-touch Deep Reinforcement Learning WLANs](https://mlsanity.com/paper/2207.06172/) (2022) - [Zero-Touch Networks: Towards Next-Generation Network Automation](https://mlsanity.com/paper/2312.04159/) (2023) - [Ensuring Fairness with Transparent Auditing of Quantitative Bias in AI Systems](https://mlsanity.com/paper/2409.06708/) (2024) - [A survey of security and privacy issues in the Internet of Things from the layered context](https://mlsanity.com/paper/1903.00846/) (2019) - [Machine Learning for Intelligent Authentication in 5G-and-Beyond Wireless Networks](https://mlsanity.com/paper/1907.00429/) (2019) ## Built on by (most cited first, 2 of 2) - [AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation](https://mlsanity.com/paper/2606.08173/) (2026) - [A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks](https://mlsanity.com/paper/2608.02274/) (2026) ## Abstract (arXiv) The transition from 5G to 6G mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for CICIDS2017 datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.