T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

Abstract

Network slicing and resource allocation play pivotal roles in software-defined network (SDN)/network function virtualization (NFV)-assisted 5G networks. In 5G communications, the traffic rate is high, necessitating high data rates and low latency. Deep learning is a potential solution for overcoming these constraints. Secure slicing avoids resource wastage; however, DDoS attackers can exploit the sliced network. Therefore, we focused on secure slicing with resource allocation under massive network traffic. Traffic-aware scheduling is proposed for secure slicing and resource allocation over SDN/NFV-enabled 5G networks. In this approach (T-S3RA), user devices are authenticated using Boolean logic with a password-based key derivation function. The traffic is scheduled in 5G access points, and secure network slicing and resource allocation are implemented using deep learning models such as SliceNet and HopFieldNet, respectively. To predict DDoS attackers, we computed the Renyi entropy for packet classification. Experiments were conducted using a network simulator with 250 nodes in the network topology. Performance was evaluated using metrics such as throughput, latency, packet transmission ratio, packet loss ratio, slice capacity, bandwidth consumption, and slice acceptance ratio. T-S3RA was implemented in three 5G use cases with different requirements, including massive machine-type communication, ultra-reliable low-latency communication, and enhanced mobile broadband.

Dynamic Network Slicing Orchestration in Open 5G Networks using Multi-Criteria Decision Making and Secure Federated Learning Techniques

“…However, none of these studies takes the security requirements of slices into account. A few recent studies considered the security metric in their evaluation such as [7,14]. In [7] authors propose a heuristic 5G core network slice provisioning algorithm called VIKOR-CNSP based on VIKOR, an MCDM approach.…”

“…However, this approach does not introduce any real-time analysis of the security requirement, and the slice acceptance ratio, which must be large for high priority traffic, is marginal. In [14] authors proposed a Traffic-aware Scheduling for Secure Slicing and Resource Allocation (T-S3RA) over SDN/NFV-enabled 5G networks using deep learning models. In this approach, user devices are authenticated using Boolean logic with a password-based key derivation function.…”

Study and Analysis of OFDM under Rayleigh fading Channel using Various Modulation Methods

“…The orthogonality of the subcarriers is achieved by the use of the fast Fourier transform (FFT), hence mitigating interference. In order to achieve flat fading in Orthogonal Frequency Division Multiplexing (OFDM), a technique involves generating low-rate data streams on subcarriers has been proposed [11]. OFDM transmission is implemented in a sequential fashion, where each symbol is transmitted individually [12].…”

Overview and Comparison of Candidate 5G Waveforms: FBMC, UFMC and F-OFDM

“…The subcarriers are orthogonal owing to the fast Fourier transform (FFT), which avoids interference. Flat fading can be achieved in OFDM by producing low-rate data streams against subcarriers [14].…”