  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,
ultrareliable low-latency communication, and enhanced mobile broadband.
