Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity. These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy. This paper proposes an industrial Internet of Things intrusion detection feature selection algorithm based on an improved whale optimization algorithm (GSLDWOA). The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to, such as local optimality, long detection time, and reduced accuracy. First, the initial population’s diversity is increased using the Gaussian Mutation mechanism. Then, Non-linear Shrinking Factor balances global exploration and local development, avoiding premature convergence. Lastly, Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in high-dimensional feature space. Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance. Compared to the traditional WOA algorithm, the detection rate and F1-score increased by 3.68% and 4.12%. On the WUSTL-IIoT-2021 dataset, accuracy, recall, and F1-score all exceed 99.9%.

Aiming at the rapid growth of network services, which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain (SFC) under 5G networks, this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment (MADDPG-SD). Initially, an optimization model is devised to enhance the request acceptance rate, minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained case. Subsequently, we model the dynamic problem as a Markov decision process (MDP), facilitating adaptation to the evolving states of network resources. Finally, by allocating SFCs to different agents and adopting a collaborative deployment strategy, each agent aims to maximize the request acceptance rate or minimize latency and costs. These agents learn strategies from historical data of virtual network functions in SFCs to guide server node selection, and achieve approximately optimal SFC deployment strategies through a cooperative framework of centralized training and distributed execution. Experimental simulation results indicate that the proposed method, while simultaneously meeting performance requirements and resource capacity constraints, has effectively increased the acceptance rate of requests compared to the comparative algorithms, reducing the end-to-end latency by 4.942% and the deployment cost by 8.045%.