In resource-constrained Internet of Things (IoT) environments, lightweight deep learning is crucial for Radio Frequency Fingerprint Identification (RFFI). However, existing lightweight designs primarily rely on group convolution with a “hard split" topology, which strictly isolates channels and blocks inter-group information sharing, impairing feature extraction. To address this, we propose overlap convolution, a novel operator employing a “soft coverage" mechanism to facilitate inter-group interaction. By enabling tunable channel overlapping, this method unifies and generalizes standard and group convolutions, restoring inter-group interaction without requiring additional mixing. We then analyze the approach by introducing structural constraint entropy as an interpretive framework to investigate the information flow capacity. Theoretical analysis demonstrates that overlap convolution offers expanded structural flexibility to achieve enhanced learning ability. Furthermore, we establish the OverlapConv framework for systematic integration of the operator into networks. Within this framework, we develop an automatic parameter acquisition strategy based on a differentiable-to-discrete transition mechanism to efficiently narrow down the search space for optimal settings. Extensive evaluations across diverse datasets (LoRa and UAV RFFI) and multiple backbones (MobileNet, ShuffleNet, and EfficientNet) confirm the method’s effectiveness as a universal plug-and-play module. Notably, our approach achieves an accuracy gain of 29.22% on the Fire block, and improves the accuracy (averaging 2.1% and 2.47% on the two datasets) of FasterNet, EfficientNet, and LMSCNet with reduced FLOPs.

This paper demonstrates that thefractal dimension of frame preambleserves as a new radiometric feature that can be used together with other known radiometric features to enhance the identification accuracy in wireless device identification. We first propose a fractal dimension estimation scheme for direct-sequence spread spectrum (DSSS) frame preamble, then provide theoretical analysis to reveal how the fractal dimension is primarily determined by the device hardware imperfections, and thus prove that the fractal dimension serves as an intrinsic radiometric feature. We further show simulation results to verify our theoretical modeling of the fractal dimension and also numerically evaluate the effects of device hardware imperfections and wireless channels on the fractal dimension. Finally, by jointly applying the fractal dimension and the five features reported in the literature, we conduct extensive experiments to demonstrate that the fractal dimension can lead to a further improvement of the state-of-the-art result in the radiometric feature-based device identification.

Convolutional neural networks have been widely used in intrusion detection and proactive network defense strategies such as network security situation prediction (NSSP). The interaction between cross-channel features and the dependencies between elements in the input data are essential factors that affect the prediction model’s performance. However, existing works have ignored these, resulting in performance that needs to be improved. To this end, we propose a GResNeSt model that combines the advantages of the global context block and ResNeSt to improve the NSSP performance. The GResNeSt model strengthens traditional convolutional neural networks in two ways: it effectively captures cross-feature interactions and obtains long-range dependencies of the input data. This enhances its performance in capturing associations among different elements, making it more effective in extracting critical information from data to identify network attacks. We used the Salp swarm algorithm to select optimal hyperparameters for improving the model’s performance. Furthermore, based on the attack impact, we calculated network security situation values of two public network datasets. Finally, comprehensive experiments on the datasets verified our model design and demonstrated that our scheme is superior to other models in terms of NSSP ability.

The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems.To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions.The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm.The model can accurately obtain the importance of each channel to assign weights to each channel while making full use of the rich contexts among neighbour keys, effectively enhancing the convolutional neural network.Furthermore, this paper calculates the network security situation value (NSSV) of the adopted datasets based on attack impact.Lastly, experiments on two cybersecurity datasets show that the comprehensive performance of the model on the three indicators of accuracy, precision and F-scores, as well as network security situation assessment, are superior to other models.