Security Challenges in 5G Network

Sahu, Gitimayee; Pawar, Sanjay

doi:10.1007/978-3-030-91149-2_4

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Establish a secure TLS connection between the client and the server; (5) Retrieve local data X i and associated labels Y i (6) Initialize: Local model parameters W i � W t ; (7) for e � 1 to E do (8) Update W i using X i and Y i through local training; ( 9) end (10) Securely send the updated local model W i to the server using the established TLS connection; (11) end (12) Aggregate and update the global model W t+1 using the received local models; (13) Send the updated global model W t+1 to all participating clients using the TLS connection; (14) end ALGORITHM 1: Federated averaging learning procedure with TLS encryption.…”

Section: Tls Version 13 For More Securitymentioning

confidence: 99%

“…However, VNFs hosted in the cloud are susceptible to a range of security threats, while integrating numerous VNFs from various vendors with diferent security measures is necessary to meet 5G requirements [5]. Tis creates a complex and diverse network, resulting in new security challenges [6], as well as exacerbating existing security vulnerabilities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model

Maiga,

Ataro,

Githinji

2024

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

The cloudification of telecommunication network functions with 5G is a novelty that offers higher performance than that of previous generations. However, these virtual network functions (VNFs) are exposed to internet threats when hosted in the cloud, resulting in new security challenges. Another fact is that many VNFs vendors with different security policies will be implied in 5G deployment, creating a heterogeneous 5G network. The authorities also require data privacy enhancement in 5G deployment and there is the fact that mobile operators need to inspect data for malicious traffic detection. In this situation, how can network traffic inspections be conducted effectively without infringing on data privacy? This study addresses this gap by proposing a novel state-of-the-art hybrid deep neural network that combines a convolutional neural network (CNN) stacked to bidirectional long short-term memory (BiLSTM) and unidirectional long short-term memory (LSTM) for the deep inspection of network flow for malicious traffic detection. The approach utilizes federated learning (FL) to facilitate multiple VNFs vendors to collaboratively train the proposed model without sharing VNFs’ raw data, which can mitigate the risk of data privacy violation. The proposed framework incorporates transport layer security (TLS) encryption to prevent data tempering or man-in-the-middle attacks between VNFs. The framework was validated through simulation using open-access benchmark datasets (InSDN and CICIDS2017). They achieved 99.99% and 99.58% accuracy and 0.048% and 0.617% false-positive rates for the InSDN and CICIDS2017 datasets, respectively, for FL. This study demonstrates the potential of hybrid deep learning-based FL for heterogeneous 5G network VNFs security monitoring.

show abstract