Power transmission lines are essential components of power systems, and their operating conditions directly affect the safety, stability, and reliability of power supply. In practical operation, transmission lines are susceptible to various fault conditions, such as short circuits, grounding faults, conductor breakage, and insulator abnormalities, due to lightning strikes, strong winds, pollution, equipment aging, and external disturbances. Conventional fault identification methods mainly depend on relay protection signals, manual inspection, and model-based analysis. Although these methods have been widely applied, they often suffer from limited adaptability, insufficient feature representation capability, and reduced identification accuracy under complex operating conditions. To improve the accuracy and intelligence level of transmission line fault diagnosis, this paper proposes a deep learning-based intelligent fault identification method for power transmission lines. The proposed method employs a deep neural network to learn fault characteristics directly from transmission line monitoring data, thereby reducing dependence on manual feature extraction. Through hierarchical feature representation and nonlinear mapping, the method can effectively distinguish different fault patterns and improve fault classification performance. In addition, preprocessing and optimization strategies are introduced to suppress noise interference and enhance model robustness under imbalanced and complex data conditions. Experimental results show that the proposed method achieves better identification accuracy, stability, and generalization performance than conventional methods. The proposed method can provide effective technical support for online fault monitoring, rapid fault diagnosis, and intelligent operation and maintenance of transmission lines.
Zhi-Wei Ni, Wen Chen, Pan Zhou et al.· European Conference on Elect...· 0 citations
Evasion attacks pose a significant threat to Network Intrusion Detection Systems (NIDS) by manipulating packets to bypass their defensive strategies. This paper presents a novel black-box evasion attack framework, BlackSlit, tailored against NIDS for encrypted traffic. Unlike traditional “buffer-and-perturb” pipelines that incur significant latency, BlackSlit enables live evasion by generating universal perturbations for incoming encrypted traffic without prior knowledge. To ensure practical applicability, BlackSlit generates packet-level perturbation sequences and imposes manipulability constraints on encrypted packet features, ensuring that the perturbations are executable at the packet level. BlackSlit comprises two core components, the Generator and the Simulator, which operate in a complementary manner. The Generator iteratively produces packet-level universal perturbations, while the Simulator mimics the behavior of black-box target NIDS to guide the update process of the Generator. Both components employ a hierarchical time-series Transformer (TST)-based architecture to account for temporal correlations intrinsic to encrypted traffic, aligning with the focus of state-of-the-art (SOTA) NIDS. This architecture ensures that temporal dependencies are effectively modeled during both perturbation generation and target system simulation, thereby enhancing the overall efficacy of evasion attacks. Evaluations are conducted on 3 real-world datasets, benchmarked against 6 leading evasion attack baselines and 9 NIDS. Results demonstrate that BlackSlit consistently outperforms state-of-the-art methods across all benchmarks. Moreover, experiments against defended NIDS confirm that BlackSlit maintains robustness.
Hua Ding, Lixing Chen, Bo Zhang et al.· IEEE Transactions on Network...· 0 citations
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