As the Industrial Internet of Things becomes increasingly interconnected with critical infrastructure, intrusion traffic exhibits characteristics such as high-dimensional redundancy, class imbalance, and temporal correlation, posing challenges for detection systems in terms of feature representation, model complexity control, and real-time performance. To address the aforementioned issues, this paper proposes an intrusion detection algorithm based on feature optimization and asynchronous advantage actor-critic learning (APA3CID). First, the raw dataset was preprocessed using methods such as label encoding and normalization. Feature selection was performed using the improved Whale Optimization Algorithm (WOA) to reduce data redundancy and eliminate irrelevant features. The samples were then serialized based on the order in which they were collected. Second, we model the detection process as a Markov decision process, use a sliding window to construct states that capture recent temporal features, and, building upon the Asynchronous Advantage Actor-Critic (A3C) framework, we incorporate an adaptive exploration mechanism to address the issues of insufficient exploration in the early training phase and unstable convergence in the later phase. Additionally, we introduce an asynchronous lag correction strategy that utilizes truncated importance weights to mitigate the bias caused by policy lag in asynchronous parallel training, thereby enhancing the stability and robustness of policy updates. Finally, experimental results show that on the X-IIoTID dataset, APA3CID achieves a 3.51% increase in detection rate and a 4.26% increase in F1-score compared to the traditional A3C algorithm. On the WUSTL-IIoT-2021 dataset, single-sample prediction takes as little as 11.56 microseconds, with Acc, DR, and F1-score all exceeding 90%. This outperforms comparison models such as LR, XGBoost, CNN, and the baseline A3C, meeting the requirements of industrial IoT scenarios for low false-negative rates and high real-time performance.

To address the problems of poor applicability of existing fault link recovery algorithms in network topology migration and backup path congestion, this paper proposes a backup path algorithm based on graph convolutional neural to improve deep deterministic policy gradient. First, the BPDM-GCN backup path algorithm is constructed within a deep deterministic policy gradient training framework. It uses graph convolutional networks to detect changes in network topology, aiming to optimize data transmission delay and bandwidth occupancy within the network topology. After iterative training of the BPDM-GCN algorithm, the comprehensive link weights within the network topology are generated. Then, according to the comprehensive link weight and taking the shortest path as the optimization objective, a backup path implementation method based on the incremental shortest path tree is designed to reduce the phasor data transmission delay in the backup path. In conclusion, the experimental results show that the backup path formulated by this algorithm exhibits reduced data transmission delay, minimal path extension, and a high success rate in recovering failed links. Compared to the superior NRLF-RL algorithm, the BPDM-GCN algorithm achieves a reduction of approximately 14.29% in the average failure link recovery delay and an increase of approximately 5.24% in the failure link recovery success rate.