A Federated Learning Approach for Improving Security in Network Slicing

Wijethilaka, Shalitha; Liyanage, Madhusanka

doi:10.1109/globecom48099.2022.10001190

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…It integrates DAG-blockchain technology with SDN/NFV and edge computing, providing a secure and reliable network slicing service. Federated learning, proposed in [59], aims to improve security in network slicing by aggregating training data from multiple slices to detect anomalies and security threats across the entire network. Simulation experiments demonstrate its effectiveness in enhancing security threat detection accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…However, the proposed platform encounters challenges in terms of ensuring high accuracy and scalability. Similarly, Wijethilaka and Liyanage [59] suggest a federated learning approach to enhance security in network slicing. However, the approach faces challenges in terms of scalability and efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, a context-aware authentication handover and secure network slicing that incorporates a DAG-blockchain in edge-assisted SDN/NFV-6G environments can enhance the security of network slicing [57]. Federated learning can also be deployed to bolster the security of network slicing [59]. By incorporating the Skunk-A Blockchain and Zero Trust Security Enabled Federated Learning Platform, the security of 5G/6G network slicing is significantly enhanced.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning-based Secure 5G Network Slicing: A Systematic Literature Review

Alanazi

2023

IJACSA

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As the fifth-generation (5G) wireless networks continue to advance, the concept of network slicing has gained significant attention for enabling the provisioning of diverse services tailored to specific application requirements. However, the security concerns associated with network slicing pose significant challenges that demand comprehensive exploration and analysis. In this paper, we present a systematic literature review that critically examines the existing body of research on machine learning techniques for securing 5G network slicing. Through an extensive analysis of a wide range of scholarly articles selected from specific search databases, we identify and classify the key machine learning approaches proposed for enhancing the security of network slicing in the 5G environment. We investigate these techniques based on their effectiveness in addressing various security threats and vulnerabilities while considering factors such as accuracy, scalability, and efficiency. Our review reveals that machine learning techniques, including deep learning algorithms, have been proposed for anomaly detection, intrusion detection, and authentication in 5G network slicing. However, we observe that these techniques face challenges related to accuracy under dynamic and heterogeneous network conditions, scalability when dealing with a large number of network slices, and efficiency in terms of computational complexity and resource utilization. To overcome these challenges, our experimentation shows that the integration of reinforcement learning techniques with CNNs, multi-agent reinforcement learning, and distributed SVM frameworks emerged as potential solutions with improved accuracy and scalability in network slicing. Furthermore, we identify promising research directions, including the exploration of hybrid machine learning models, the adoption of explainable AI techniques, and the investigation of privacy-preserving mechanisms.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Machine Learning-based Secure 5G Network Slicing: A Systematic Literature Review

Alanazi

2023

IJACSA

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“…The authors of [15] have developed a DL module to detect DDoS attacks, automatically creating a sinkhole-type slice with a small portion of physical resources and isolating the malicious users within this slice to mitigate the attackers' action. The authors of [16] proposed an FL-based architecture that coordinates security orchestration to centrally handle security operations of network slicing while preserving data privacy. However, the previous works [13][14][15] have only focused on attacks on the 5G-Core focusing DoS attacks, and without considering 5G-V2X verticals.…”

Section: Related Workmentioning

confidence: 99%

“…It proposes collaborative learning based on federated and ensemble learning while considering slice isolation and privacy preservation. Specifically, unlike [16,19], our scheme leverages multiprocess federated learning and complies with the current 5G standards and promises for 6G networks. Table 2 describes the frequent abbreviations used in the paper.…”

Section: Related Workmentioning

confidence: 99%

Multi-Process Federated Learning With Stacking for Securing 6G-V2X Network Slicing at Cross-Borders

Boualouache,

Jolfaei,

Engel

2024

IEEE Trans. Intell. Transport. Syst.

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Being part of the 6G ecosystem vision, Connected and Automated Vehicles (CAVs) will enjoy sophisticated tailored services offering road safety and entertainment for users. As one of the 6G cornerstones, Network Slicing (NS) allows the creation of various customized 6G-V2X (Vehicle-to-Everything) use cases on the same physical infrastructure. However, 6G-NS advances can open up breaches to cyber-attacks aiming to break 6G-V2X Network slices to inflict maximum damage on CAVs and their users. Crossing borders, where CAVs leave their V2X-NS (V2X Network Slice) in the Home Mobile Network Operator (H-MNO) toward a similar V2X-NS in the Visited MNO (V-MNO), is an attractive opportunity to exploit by attackers. Detecting and mitigating attacks, in this case, becomes a priority, confronted by NS requirements and MNOs not ready to share their private data. To this end, this paper proposes a 3GPP-compliant privacy preservation collaborative learning scheme for 6G-NS security, focusing on V2X-NS cross-border areas. Our scheme leverages multi-process Federated Learning (FL) architecture to build efficient V2X-NS security-related models while preserving 6G V2X-NS isolation. In addition, it uses differential privacy-enabled stacking to build up attack detection knowledge at the V2X-NSs and MNOs levels while ensuring privacy preservation. We conducted an experimental study on the 5G-NIDD dataset, which is one of the most realistic publicly available 5G datasets. Our results demonstrate that multi-process FL with stacking can deliver high accuracy while ensuring isolation between 6G-V2X-NSs and privacy preservation between H-MNO and V-MNO.

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