Small-target detection in sea clutter is a key challenge in marine radar surveillance, crucial for maritime safety and target identification. This study addresses the challenge of weak feature representation in one-dimensional (1D) sea clutter time-series analysis and suboptimal detection performance for sea surface small targets. A novel dual-feature image detection method incorporating an improved mobile vision transformer (MobileViT) network is proposed to overcome these limitations. The method converts 1D sea clutter signals into two-dimensional (2D) fused images by means of a Gramian angular difference field (GADF) and recurrence plot (RP), enhancing the model’s key-information extraction. The improved MobileViT architecture enhances detection capabilities through multi-scale feature fusion with local–global information interaction, integration of coordinate attention (CA) for directional spatial feature enhancement, and replacement of ReLU6 with SiLU activation in MobileNetV2 (MV2) modules to boost nonlinear representation. Experimental results on the IPIX dataset demonstrate that dual-feature images outperform single-feature images in detection under a ${10}^{-3}$ constant false-alarm rate (FAR) condition. The improved MobileViT attains 98.6% detection accuracy across all polarization modes, significantly surpassing other advanced methods. This study provides a new paradigm for time-series radar signal analysis through image-based deep learning fusion.

Modern network attacks are becoming stealthier and smarter. Attackers use encryption to cover up malicious traffic, which makes it really hard to detect. To solve this problem, this paper introduces a new model called Graph Convolutional Network with Multi-Head Attention (GCN-MHA). The goal of this model is to improve how we find and sort encrypted malicious traffic. First, we turn network traffic into a “graph”—this helps capture its structural and time-related features. Then, our GCN-MHA framework uses graph convolutional layers to learn spatial information. A multi-head attention mechanism helps it focus on the most important features. When tested on the ISCX-VPN2016 dataset, the model achieved an overall high accuracy of 98.79% and a recall rate of 99.24% under six categories of malicious traffic. We also performed cross-validation on two other datasets: USTC-TFC2016 and CIC-Darknet2020. These tests showed that the model has strong generalization ability on different data.

Network Intrusion Detection (NID) is essential for cybersecurity because network traffic and security threats are growing rapidly. However, current NID methods suffer from a critical limitation: they face inherent difficulties in jointly modeling global contextual dependencies and local fine-grained temporal features within network traffic, which leads to suboptimal detection accuracy. To address this, this paper proposes the Global and Local Temporal (GLT) NID model. This model utilizes the self-attention mechanism of the Transformer to extract global dependencies and complex feature correlations from network traffic sequences while combining the bidirectional recurrent structure of BiLSTM to capture local temporal features of traffic data. Additionally, to address the class imbalance issue in intrusion detection datasets, this paper introduces a power-smoothed soft weighting strategy for optimization. We tested the GLT model on the NSL-KDD and UNSW-NB15 datasets. On the NSL-KDD dataset, the model achieved 99.57% accuracy and 0.32% false positive rate. On the UNSW-NB15 dataset, it achieved 88.04% accuracy and 0.76% false positive rate. Compared to current state-of-the-art techniques, it demonstrates higher detection accuracy and lower false positive rates.

This paper proposes a Graph Attention based Convolutional Neural Network (GAT-CNN) for encrypted malicious traffic classification and detection, which improves the performance of network encrypted malicious traffic detection, recognition, and classification. By establishing a connectivity diagram between nodes, the spatiotemporal features in network traffic records are strengthened, providing a feature basis for intrusion detection. Using graph convolutional neural networks to dynamically aggregate and extract node behaviors in the graph, generate feature representations with category discrimination ability, and allocate different weights through multi head attention mechanism to achieve accurate classification of network intrusion behavior. To verify the effectiveness of the method, experiments were conducted on the ISCX-VPN encrypted traffic dataset, with a detection accuracy of 98.79% and a recall rate of 99.24%, demonstrating that the proposed method exhibits good performance in detecting malicious traffic.