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Network slicing and resource allocation play pivotal roles insoftware-defined network (SDN)/network function virtualization (NFV)-assisted5G networks. In 5G communications, the traffic rate is high, necessitating highdata rates and low latency. Deep learning is a potential solution forovercoming these constraints. Secure slicing avoids resource wastage; however,DDoS attackers can exploit the sliced network. Therefore, we focused on secureslicing with resource allocation under massive network traffic. Traffic-awarescheduling is proposed for secure slicing and resource allocation overSDN/NFV-enabled 5G networks. In this approach (T-S3RA), user devices areauthenticated using Boolean logic with a password-based key derivationfunction. The traffic is scheduled in 5G access points, and secure networkslicing and resource allocation are implemented using deep learning models suchas SliceNet and HopFieldNet, respectively. To predict DDoS attackers, wecomputed the Renyi entropy for packet classification. Experiments wereconducted using a network simulator with 250 nodes in the network topology.Performance was evaluated using metrics such as throughput, latency, packettransmission ratio, packet loss ratio, slice capacity, bandwidth consumption,and slice acceptance ratio. T-S3RA was implemented in three 5G use cases withdifferent requirements including massive machine-type communication,ultrareliable low-latency communication, and enhanced mobile broadband.

T-s3ra: traffic-aware scheduling for secure slicing and resource allocation in sdn/nfv enabled 5g networks