Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. To improve the accuracy of dual fault diagnosis in the DN, this study optimizes BPNN by combining the genetic algorithm (GA) and cloud theory. The two types of BPNN before and after optimization are used for single fault and dual fault diagnosis of the DN, respectively. The experimental results show that the optimized BPNN has certain effectiveness and stability. The optimized BPNN requires 25.65 ms of runtime and 365 simulation steps. And in diagnosis and positioning of dual faults, the optimized BPNN exhibits a higher fault diagnosis rate, with an accuracy of 89%. In comparison to ROC curves, the optimized BPNN has a larger area under the curve and its curve is smoother. The results confirm that the optimized BPNN has high efficiency and accuracy.

As the proliferation of 5th generation mobile communication technology (5G) accelerates the adoption of Internet of Things (IoT) applications, building robust and secure communication channel becomes increasingly crucial with the exponential growth of connected devices. The third generation partnership project (3GPP) has established security standards for the 5G systems, including mechanisms, such as 5G-authentication and key agreement (5G-AKA), which enable secure sessions with untrustworthy participants or insecure channels. The private key with untrustworthy parties has been independently or transmitted through insecure channels, which may involve a risk of information leakage in 5G-AKA. Motivated by this challenge, we propose a practical and secure dual-server key agreement scheme based on password authentication for the IoT devices in the 5G networks. The scheme ensures secure reliable key storage and key transmission, mitigating the risks associated with key information leakage through a dual-server architecture and a three-lock security policy. Importantly, we avoid ownership of the complete key by any untrustworthy entity in an insecure 5G network to ensure key security. The scheme can be resilient to various security threats prevalent in 5G networks through rigorous formal security. We analyse the communication and computational loads to illustrate the protocol’s practicality and efficacy.

Current multi-stream convolutional neural network (MSCNN) exhibits notable limitations in path cooperation, feature fusion, and resource utilization when handling complex tasks. To enhance MSCNN’s feature extraction ability, computational efficiency, and model robustness, this study conducts an in-depth investigation of these architectural deficiencies and proposes corresponding improvements. At present, there are some problems in multi-path architecture, such as isolated information among paths, low efficiency of feature fusion mechanism, and high computational complexity. These issues lead to insufficient performance of the model in robustness indicators such as noise resistance, occlusion sensitivity, and resistance to sample attacks. The architecture also faces challenges in data scalability efficiency and resource scalability requirements. Therefore, this study proposes an optimized model based on a dynamic path cooperation mechanism and lightweight design, innovatively introducing a path attention mechanism and feature-sharing module to enhance information interaction between paths. Self-attention fusion method is adopted to improve the efficiency of feature fusion. At the same time, by combining path selection and model pruning technology, the effective balance between model performance and computational resources demand is realized. The study employs three datasets, Canadian Institute for Advanced Research-10 (CIFAR-10), ImageNet, and Custom Dataset for performance comparison and simulation. The results show that the proposed optimized model is superior to the current mainstream model in many indicators. For example, on the Medical Images dataset, the optimized model’s noise robustness, occlusion sensitivity, and sample attack resistance are 0.931, 0.950, and 0.709, respectively. On E-commerce Data, the optimized model’s data scalability efficiency reaches 0.969, and the resource scalability requirement is only 0.735, showing excellent task adaptability and resource utilization efficiency. Therefore, the study provides a critical reference for the optimization and practical application of MSCNN, contributing to the application research of deep learning in complex tasks.

With the rapid development of big data, artificial intelligence, and Internet technologies, human–human contact and human–machine interaction have led to an explosion of voice data. Rapidly identifying the speaker’s identity and retrieving and managing their speech data among the massive amount of speech data have become major challenges for intelligent speech applications in the field of information security. This research proposes a vocal recognition technique based on information adversarial training for speaker identity recognition in massive audio and video data, as well as speaker identification when oriented to the information security domain. The experimental results show that the method projects data from different scene channels all onto the same space and dynamically generates interactive speaker representations. It solves the channel mismatch problem and effectively improves the recognition of the speaker’s voice patterns across channels and scenes. It is able to separate overlapping voices when multiple people speak at the same time and reduce speaker separation errors. It realizes speaker voice recognition for the information security field and achieves a recall rate of 89% in a large database, which is of practical value for the intelligent application field.

In low-altitude networks, various aerial platforms such as autonomous aerial vehicles and airships cooperate to provide services including real-time monitoring, emergency response, and data collection. These platforms often operate with limited computing resources, restricted energy supply, and unstable wireless connectivity, making centralized data processing inefficient and prone to privacy risks. Federated Learning (FL) provides a promising solution by enabling multiple platforms to collaboratively train a shared model without exchanging raw data. However, the presence of Byzantine clients and a potentially malicious server poses serious threats to the robustness and trustworthiness of FL in such environments. Existing Byzantine-robust FL methods typically assume a semi-honest server and rely on auxiliary information such as clean datasets or known attacker ratios, which limits their applicability in dynamic and non-IID scenarios. In this paper, we propose zkFL, a Byzantine-robust FL framework that embeds zero-knowledge proofs to ensure verifiable aggregation under a malicious server. ZkFL allows clients to verify the correctness of server-side aggregation and dynamically adjusts client weights based on inference-guided detection, without relying on external datasets. Each round includes a zk-SNARK proof to guarantee aggregation integrity while preserving gradient privacy. Experiments demonstrate that zkFL exhibits strong robustness and verifiability in both IID and non-IID settings, outperforming prior methods, even in the presence of a malicious server.